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May 10, 2014 by Andrew Feeney 1 Comment
A Day at the Creek
Photograph: IMAGE OF THOUGHTS @Imageofthoughts
This post the 3rd installment to: A new urban politics II: the urban paradox — how determines what, and A new urban politics.
"Stop thinking start doing: we need more experimenting on the ground", is what Bert van Lamoen explained in response to someone's question about the meaning of his preceding tweet that said "Enough is enough".
Of course one can't keep on doing without soon thinking. Try standing on one leg and raising the other, a simple enough exercise, and see how long it takes for your mind to be confronted with the burning pain in your supporting leg, and for that action to cease and another to take its place.
Actions not only follow thinking, they induce thinking. And the thinking generated from action is relative — to the action: "I can't keep it up"; "This is killing my leg"; "I hate my physio". The thinking leads to new actions (recovery) which in turn induce new thinking: "My legs are weak"; "I need to do this more" …
This recursive practice, this learning by doing is the essence of (experiential) learning and is, or at least once was, natural to us all. What is not natural is this type of learning in either education or professional practice today.
It is not simply that the breaking of learning into individual subjects of discrete disciplines reduces any given real-world experience to just one lens (a reductionist approach to complex reality), didactic methods of teaching, in the main, develop only the abstract, and only, I would argue, information, not vital knowledge. What's more, professional practice, by definition, is just the accommodation of discipline theory. What of the development of that theory? What of the development of practice?
Unlike information, knowledge is knowing. It is relative, not static. How could we truly know something without experiencing it? How could we understand the pain of standing on one leg without really trying it? If we didn't try it, how would we really know how we would react?
The generation of knowledge is vital to learning, and learning is vital to the generation of knowledge. Knowledge and learning are interdependent because there are two types of knowledge generated by, (and required of), learning (Fig 1): 'finding out' and the generation of theoretical knowledge in both the real (i) and conceptual (ii) worlds, and 'taking action' and the generation of practical knowledge in both the conceptual (iii) and real (iv) worlds.1
Figure 1: Learning as the flux between 'finding out' and 'taking action' (adapted after Kolb)
Source: R. Bawden, 'The Hawkesbury Experience: Tales from a road less traveled'
For a subject as complex as the city, knowledge is vital. Yet the disciplines that are supposedly about the city (i.e. urban design, urban planning, architecture and landscape architecture) remain defined by their own particular weltanschauung, one which has them firmly declaring themselves as disciplines of the 'built environment'. What's more, projects across these disciplines, in the main, remain outside the core of how cities exist. The urban question upon which all cities are founded (i.e. how broad forms of wealth can be generated through the exchange of commerce and culture) is not attended to through a 'city' focus. The 'built environment' is not something to be designed, at the outset. The built environment (city) follows the urban; 'designing' bricks and mortar, infrastructure, housing, and parks deals with urban effects over urban causes. In traditional built environment education, even 'design' projects that are based on 'real' sites for which 'designs' are proposed, sometimes presented, to 'real' audiences only constitute the abstract. This is because the project (and, therefore, any work) is abstracted from how reality, in these cases, the city, exists. And for practitioners, especially those of the built environment professions who, somehow, have hegemony of professional involvement in advising on anything to do with the city, practice has drifted far, far away from, yet at the same time increasingly reductive to, their respective theoretical foundations, however well defined.2
For students of the city (in this case I mean those formally enrolled in education about the city) learning — as a process — is vital to the explanation of a discipline and to its application. Perhaps this is why, for urban design in particular, there is, as Alexander Cuthbert points out, a glaring lack of theoretical rigour about the discipline, let alone any consistency in the shallow theories in urban design.3
Whilst the demands of neoliberal capitalism appear to be forcing our big tertiary university educational institutions in Australia into the education business (of attracting students), rather than assisting them to be in the business of education (of the student), the practice of the built environment professions appear to be similarly consumed by the market they are in. While all professions are defined universally by the provision of advice, the advice provided by the built environment professions is inextricably linked to, in fact consumed by, neoliberal capitalist ventures, namely property development. In our current market, property development trades off the speculative and for little collective value. In turn, these professional services could be said to consume the market: the very reliance on property development as a source of work and as a basis of commercial viability and professional prosperity and sense of legitimacy. Both education and practice have become products of consumption, and consumptive products. Both appear complicit with everything their discipline foundations (society and nature, essentially) are counter to. In an age where it is obvious the world's social and environmental battles will be won or lost in cities, what are we to do about the education of designers for our urban futures? Just what are we to do about the definition and explanation of the urban as a discipline, and its teaching? What of its application in practice?4
For effective learning (read also practice!) one must be able to generate both conceptual and practical knowledge. This recursive process not only generates learning — about the discipline and about practice — but learning about learning: the discipline of the discipline and the 'practice of practice'.5 Known as praxis this 'emergent property' was the foundation of my teaching of the #UPSTREAM studio I will now introduce as explanation and demonstration of that by which this the third installment in the series is known.
Figure 2: Praxis as an emergent property
#UPSTREAM
#UPSTREAM is metaphor. It is the title of the design studio I ran at the RMIT School of Architecture and Design, centred on the improvement of Moonee Ponds Creek, a 28-km length (approx.) waterway, in Melbourne. The meaning of the title is especially rich; the words of the poster I used to promote the studio, for which a mix of Landscape Architecture final year undergraduate and 1st year Masters students selected through ballot, were:
Moonee Ponds Creek is a highly modified and disturbed regional waterway that crosses the boundaries of four separate local government agencies, has varied land status and multiple land owners and stakeholders. For the most part, it is essentially a trapezoidal drainage channel designed to convey water in the fastest way possible, but with very little function for, or value to, anything else. How ought a landscape architect to go about improving it?
Using the Moonee Ponds Creek in its entire catchment as the project platform, the studio will explore design as verb, as distinct from the noun. This orientation will force a questioning of just what it is that is being designed. Is the creek a formal condition and phenomenon that is in need of change, or is its condition a symptom of something else that may need to be changed? What could that something else be? And if design need not be formal in representation, what, then, is a designer?
The studio will promote the value of strategic design as a necessary and vital process of exploring how, by asking the right questions, we can work "upstream" of traditional design-object-to-defined-brief expectations, and be the Change Agents for influencing the conditions to create new "ecologies" for the change we need.
So 'upstream' is a waterway metaphor for a waterway. Just as one must search upstream of any point in a waterway in order to understand the dynamics of a whole-of-catchment phenomenon, so too must one search upstream, figuratively, of traditionally defined problems and project briefs.
It should be said that the literal hydro-eco upstream consideration, important as it is to an understanding the hydo-eco dynamics of a waterway, is subordinate to the metaphoric upstream enquiry, in the sense that the metaphor is a meta-design consideration where the technical is subordinate to the political economic context by which the city and its component waterway exists. This project adopted a political economic lens in order for students to explain the spatial and, especially, the hydro-eco technical. "Always design a thing by considering it in its next larger context" argued Eliel Saarinen, but in the complex world of cities (not objects) it is the context that needs (re)designing if objects are to be validated.6 Teach a man how to fish, for sure, but who says man must eat fish? What happens when all the fish are caught? And what of feeding women? The upstream metaphor forces enquiry into the source or cause of the varied factors that define the situation being investigated. The Moonee Ponds Creek exists as part of, and for, Melbourne. Ultimately, Moonee Ponds Creek is about Melbourne.
All space is produced; Moonee Ponds Creek as the material evidence of individual, disparate capital works projects (with the proposed East West Link elevated roadway shown, left, in image)
Base photograph: Martin Rowland (martyr_67)
The nature of the project: Situation improvement
The project introduced the idea that design is a process not limited to 'solving problems', but a liberating notion relevant to 'situations' of interest, concern and unease. (The future, in this sense, is an 'emergent property' of concern, and rightly so, but the future is not some construct to be 'designed'.) The frame for enquiry, therefore, recognised the true value of design in starting with desire, from a position of hope, and value creation, not an analysis of 'issues', or from a position of deficit. We referred to the overall project endeavour as 'situation improvement'. This is distinct from defining projects through the provision of some predetermined 'brief' and its prescribed component 'aims', 'objectives', 'methods' and 'products', for which there was none provided in this studio. Importantly, for landscape architects, it discards the traditional sequence of arriving at situations after some level of (pre)determination — either in aim or delivery method. It destroys the traditional sequence of analysis > issues > concept > formal depiction incremental linearity, and especially so applied to the literal 'landscape'. It makes the design process strategic, rather than making the design process prioritise the 'strategic plan' as a leading 'solution' (e.g. the traditional grand, long-term, investment-dependent but politically naive 'Master Plan'). In short, it forced the students to search upstream.
And so the #UPSTREAM studio project asked students to define 3 things:
1. The 'creek';
2. Improvement, and
3. Their role (as a landscape architect, not their landscape architect role).
An additional requirement was for the students, upon defining the above, to demonstrate completion of the cycle(s) of learning. For this I charged the students with a 4th challenge:
4. Carry out real-world action(s).
Recognising that the tradition of landscape architecture education and practice (not to mention the plethora of project reviews found in contemporary journals) would limit students' perception of the creek to a physical phenomenon — some technical spatiality in need of 'design'— and their role as the author of that design, the studio, instead, commenced with an exercise to abruptly and radically shift this view. The first exercise was specifically undertaken before a visit to the creek and before the 4-point assignment as defined above was revealed.
1st Studio Exercise
Local government authorities (4 no.) in the catchment of Moonee Ponds Creek
After a general introduction to the extent of the creek catchment, its multiple local authority boundaries, varied land status and ownership, flooding conditions, pollution, the nature of the local authorities as cultural organisations, and illumination of the many independent and disparate capital works projects that have 'made' Moonee Ponds Creek (using photomontage above), I asked a series of questions by referring to the same base image of the creek used in the photomontage (see below):
Moonee Ponds Creek, looking upstream, within the City of Melbourne (Photograph per montage above.)
Photograph: Martin Rowland (martyr_67)
In direct reference to the photo, each student was asked the following:
1. Define what 'improvement' may mean relative to Moonee Ponds Creek?
2. What definition of Moonee Ponds Creek does this imply? (Define the creek.)
3. Outline what changes occur & what are the drivers of change?
4. What spatialities do you see — describe them?
5. How do these spatialities exist? (What produces these spatialities?)
2nd studio exercise
Undertaking the exercise prior to a visit to the creek enabled students to approach their first site visit the following week with a new frame. An additional exercise was given on completion of the site visit, for individual response via studio presentation the following week. Building on the earlier work, this second exercise re-captured the above 5 questions and added the following:
6. Who are the people in the situation improvement opportunity? Can you "categorize" them?
7. Is there an audience for situation improvement?
8. Is there a client?
9. Who are the benefactors?
10. What does public space mean to you?
11. What is sustainability?
12. What sustainability issues are at play in the MPC?
The #UPSTREAM project
The 18 students in the studio were directed to self-organise into 6 groups of 3. This enabled exposure of students to real-world practices of communication, politics, collaboration, and management, as well as reducing the otherwise potentially exhausting impact of both research and application activities on existing communities had the students worked on the same project individually. It also enabled me as studio leader/ teacher/ coach to have a far greater interaction with all students and their work through their group activities, than had I tried to interact with 18 students individually over limited studio contact time. (It should be noted that in assessment terms, a part overall score was defined for individual student contribution, in order to incentivise and moderate any under achieving or exceptional contribution of individuals.)
#UPSTREAM students in studio
I will draw on the project work from across the student groups for illustration and discussion of each of the 4 project requirements, while enabling the 'new urban politics' narrative to remain 'emergent'. I hope this to be a more useful reference to the student work than if I were merely to describe the work as independent project reviews. For reference to each respective student group, I will use their own group name which they determined through the process of their learning. (The purpose and meaning of their elected group names will become clearer as this post unfolds.) I have not included work from all 6 groups, due to the limitations of this post, but would encourage readers to go to the student-formed project website that contains work and social media links from all the 6 groups.
A new urban politics and the meaning of 'new'
I referred to a new urban politics in the previous post as the preeminent consideration in cities and in making (any) change. I defined a new urban politics as 'a reflective description, an explanation, a condition, a capacity, the determinate and the only measure, all in one'. But I withheld an explanation of what constituted the 'new'. It was the work of the students in attending to the 4 requirements of the project that generated this concept for me, aided by the very astute and insightful input from Ingo Kumic, a guest I invited to mid and final semester 'crits' and who I have quoted in previous EnvisagedCity posts, as well as a lecture by Indy Johar, and many engaging discussions with my colleague Matthew Willcox at the City of Melbourne. The 4 requirements allude to the meaning of urban politics and define and demonstrate the meaning of the 'new', viz:
Defining the creek: the existing urban politics
Defining improvement: a new urban politics
Defining the (designers') role: the design challenge in bringing about a new urban politics
Taking real-world action: orchestrating a new urban politics.
So a 'new' urban politics is an improved urban politics. Again, design is engaged not for identified problems, but to bring about 'situation improvement' — a condition that can only be determined by the politics in question.
1. Defining the creek: the existing urban politics
Landscape architecture and civil engineering are the two disciplines most responsible for the execution of changes made to waterways; the former profession only more recently having any agency in the determination of waterway function and appearance. Despite Landscape architecture's contribution to a broader understanding of the potential multiple functions and values of waterways, especially in comparison to the mono-functional trapezoidal profiles created by reductionist engineering work prior, both professions have, essentially, dealt with waterways in their technical capacity only.
Map: Surveyed & drawn by Robert Russell
Source: State Library of Victoria
But the technical aspects of waterways have always been subordinate to, determined by, and dependent upon the political economy of urban development. In the case of Moonee Ponds Creek, specifically, the nomenclature applied to it is revealing. Moonee Ponds Creek is the colloquial name now commonly used in reference to what was reported to originally be an ephemeral series or chain of ponds that culminated in what was first known as West Melbourne Swamp. Yet a search on Google maps identifies the lower portions of the waterway (and the entire waterway within the City of Melbourne, and corresponding in location to the former swamp), in name as 'Railway Canal'. In type, this now tributary of the equally disturbed Yarra River varies from a narrow carving in rapidly suburbanising open pasture paddocks in the upper catchment, to a narrow to wide open concrete stormwater drainage channel in the lower catchment. Where there was once bush, there became farmland; now there are suburban housing estates. Where there was swamp, there is now a channel. Where there was a nearby tidal river, there is now confluence with what is Docklands Harbour. Moonee Ponds Creek is anything but the creek by which it is most commonly known, and bears absolutely no resemblance to its original condition, function or values.
Pile-driving for elevated roadway of mouth of Moonee Ponds Creek, looking north
Photograph: Ian Hill, 1962
Irrespective of what name we give this much altered waterway, there is no one waterway with which we can identify with, or by which it can be identified. This is because there is no such thing as one place. Place is relative — to the meaning derived by people. And 'people' are comprised of individuals. As beautifully captured by former colleague Marty Rowland, with me on his 1st inspection of the creek by bicycle, an over-shadowed, polluted, weed-infested drainage ditch beneath the thousands of passing motorists on the elevated CitiLink viaduct, above, has significant meaning to one fisherman (that we know of).
No one place: a lone fisherman beside a forest of columns that support the CitiLink motorway over the Moonee Ponds Creek
For the purpose of this project and post, the waterway has and will be referred in name as Moonee Ponds Creek. But just how this waterway ought to be defined was the students' first project requirement.
Unlike traditional built environment disciplines which adopt a site-based or physical place-based approach to enquiry, the students grew to understand the physical creek as a product of the political economic. Yet by commencing investigation through observation of the physical, e.g. the very obvious rubbish in the water, the group doing so was able to move upstream to the source or core of the rubbish symptom (not 'problem'). In doing so, students were able to map human factors: values, relationships, power, and decisions that were responsible for what they saw. The creek was generally defined by the groups as a state by which the creek is perceived, valued, thought about, and, from which change is made: i.e. through urban politics. Space was understood as a product of the political economy of development.
TROJAN ART @Trojantrashart
Moonee Ponds Creek as the separation, through levels of trust and power, between decision-makers and citizens
IMAGE OF THOUGHTS @Imageofthoughts
Moonee Ponds Creek as product and symptom of social relations
DOMINO EFFECT @COACHMPC
Moonee Ponds Creek as state government-dominated urban politics, with the development plan prioritised and ordained
2. Defining improvement: a new urban politics
Having defined the creek in its political economic context, the students were able to avoid the traditional process of prioritising the image (aesthetic — technical or illusionary) of the object (creek — physical), and how the object exists (technical), over that which produces it and why. Improvement was generally defined among the groups as a state by which the creek is mutually perceived, valued, thought about, and, from which change is made: i.e. a new urban politics. The difference between the current urban politics and the 'new' is in representation, forms and relations of power, trust, and the cultures of decision-making.
An improved Moonee Ponds Creek as balanced representation in decision-making
An improved Moonee Ponds Creek as a re-vamped decision-making platform
An improved Moonee Ponds Creek as a balanced communal capacity to determine a Development Plan
3. Defining the (designers') role: the design challenge in bringing about a new urban politics
Recognising that the creation of a new urban politics is a design challenge of process, not object, the students, generally, were able to see that the challenge for themselves and, therefore, for landscape architects and designers was in influencing these circumstances. The traditional definition of design as noun, and designer as author would not do. The demands of shifting the existing systems of power required a different type of designer, and with a different value base and proposition, and with a different focus, process and toolkit. The challenge of bringing about a new urban politics is itself a political one. The notion of designer as author is subordinated to the designer as Agent of Change; the 'mastermind' to the facilitator; the dictator to the influencer; the owner to the enabler; the instructor to the coach, and so on.
Designer as mediator of trust and power in the relation between decision-makers and citizens
Designer as mediator towards a new form of decision-making
Designer as power coach
4. Taking real-world action(s): orchestrating a new urban politics
Of course all this is just theory, without real world action. But, as Kurt Lewin said, "there is nothing as practical as good theory"7. It must be noted that the process revealed thus far was largely developed through real-world observation (site), divergent reflection (questioning), abstract assimilation (mind mapping), and convergent conceptualisation (synthesis and propositional systems modelling). It required accommodation in the real-world to test theory and bring about a new situation from which to learn. For this there was no one grand action, but rather a series of progressive and iterative actions, generally small at first, and rapid and low-cost. Such action tested assumptions, as well as creating new conditions that altered the trajectory of approach and introduced new players of value.
In the entire student work there is a strong community activism through participatory design. Drawing, in particular, on the work of Natalie Jeremijenko and Usman Haque that I introduced, students were able to use the physical qualities of the creek as the very aesthetic with which to generate an awareness and education and learning among communities, not just of the creek as a hydro-eco system, but of the communities themselves.
Establishing, strengthening and challenging relations to bring about a new urban politics
Strategy as performative action
Iterative interventions with specific intents
Bike path survey
Trojan Raft flyer
Targeted communities
Participatory Design
A TrojanTrash Artwork returning the rubbish to the creek from whence it came
In keeping with the praxial nature of the studio, the students were, finally, asked to reflect on the final action they took, in order to assess their own agency and learning. Were they 'successful'? Were their assumptions validated? What would they do next time? What would they do next?
A different power source, intent and effect
By enabling new players to be engaged in new ways with the everyday, their communal involvement generated social capacity and political capital for change, not least in the students themselves. How to earn and respect this political capital, and then influence social relations from which representation and mediation of power are productive toward the collectives upon which society relies, is the delicate challenge to, and essential professional duty of, the designer/ change agent.
And I would say, that in our time of great environmental uncertainty, any notion of sustainability cannot be appreciated without first a biological empathy with the earth we are inextricably connected to and dependent upon, even the fragile natural qualities of an unloved stormwater drain. For love has no single, identifiable reason for being; it just is. It cannot be explained, but it cannot be out-argued or extinguished. I am sure that a deep concern for the environment can be awakened in all of us because of what we are. The challenge is how to awaken it. While new forms and public availability of data are enabling us to see and measure the world in ways we previously could never have imagined, we must remember that all data is but an abstraction of reality, not reality itself. I believe it is through contact with phenomena that our (re)connection to, and respect for, nature can be made.
Needless to say (and as I often said to the students during studio), the improvement of Moonee Ponds Creek is a long-term ambition, in scope and process. We didn't have the luxury of a long-term time-frame with which to influence a new urban politics that is required to counter the very ugly, undemocratic (capital-P dominated) urban politics which continues to stifle the needs and voices of communities, and which denies the value of the ecological commons. (I refer, directly, to the Victorian state government's East-West Link project that directly impacts the Moonee Ponds Creek that I mentioned in the first of this post series on a new urban politics. For a sad account of the current state of urban politics, read Andrew Kelly's 'Address to the Comprehensive Impact Statement (CIS)', 10 April 2014.)
Yet, as Richard Bawden once said, "no time is ever wasted", and I am grateful for the opportunity to work with such an incredibly eager, brave, courageous and appreciative class of students that made #UPSTREAM. For without the studio I may, today, still be pushing a line that says strategic purpose is the preeminent consideration in strengthening community futures, rather than accepting strategic purpose as a vital emergent property of those from whom a purpose may only be purposed and validated.
Change is happening off this sheet
End of year Landscape Architecture exhibition: use of the prescribed 1 x A0-width sheet. #UPSTREAM panel (centre left), and adjacent digitally displayed images of student modelling and action (centre right)
1. Richard Bawden, The Hawkesbury Experience: Tales from a road less traveled, paper prepared for the "Workshop on New Directions in Agroecology Research and Education", University of Wisconsin-Madison 29-31 May, 2002. Later published as: Bawden, R.J. (2005) "The Hawkesbury Experience: Tales from a road less traveled." Chapter 14 in The Earthscan Reader in Sustainable Agriculture, J. Pretty (ed), Earthscan (London).
2. Despite the hegemony of the 'built environment' professions in the making and re-making of the city over the last few decades, the current development of design in business schools, and the notion of the 'smart city' (read citizen), demonstrate encouraging signs of 'strategy' and citizen empowerment respectively, especially in ways which could never be imagined by focusing on form and the technical.
3. Alexander Cuthbert, Understanding Cities: Method in urban design, Routledge, (Oxon), 2011.
4. In Understanding Cities, Method in urban design, Alexander Cuthbert makes the sustained case for the 'field' of urban design to be redirected toward the social and economic sciences as a way to better position it in relation to spatial political economy. I have questioned the legitimacy of 'transdisciplinary' design learning and practice as an apparent break-through in traditional silo-ed approaches to design education and practice (and I don't suggest a breakthrough isn't needed), in a previous post (2nd para.), simply on the grounds that if we are talking about design, then working across the fields is inherent, and especially so if the focus of the design in question is the urban. I would simply rather define the meaning of concepts, and perhaps revive original meanings of old terms, than to seek new terms that appear tautological.
5. Richard Bawden, The Hawkesbury Experience: Tales from a road less traveled, op cit.
6. Eliel Saarinen, Architect (1873-1950), as quoted in Future Practice – Conversations from the Edge of Architecture, by Rory Hyde, 2013, Routledge, New York.
7. Kurt Lewin, as quoted by Peter Checkland in a lecture at University of Western Sydney, circa 1986.
#UPSTREAM Studio details
University: RMIT
Faculty: School of Architecture and Design
Level: Upper Pool
Studio: Landscape Architecture Design Research
Duration: Spring Semester 2013
Time: Fridays 9:30am – 1:30pm
Room: 08.12.36
UPSTREAM student projects website: http://rmitstudio.wix.com/upstream
Gagani Warnakulasooriya
Yi Ren Sun
Jiayu Hu
Candice Qih Yu Teok
Mohamad Dzulfadzli Baharudin
Dong Woo Kwak
PICASSO'S IDEAL CREEK @Picasso_MPC
Hengwei Sun
Jin Wang
Yunlan Fan
SOWING @Sowing_Upstream
Hui Shuai
De Qiang Yu
Yuxuan Yang
SPONDGE @UpstreamSpondge
Dimitri Russo
Ye Ma
Kyle Bush
Huangguan Lu
Rajiv Bhageerutty
Mid and end of semester 'crit' guests:
Dr Ingo Kumic, Knox City Council
Alastair Kilpatrick, RMIT
Studio learnings: Critical thinking, Strategy, Systems, Spatial political economy, Brand, Participatory design, Change management, Governance, Ecology, Hydrology, Social media, Communication.
Filed Under: Design Tagged With: #UPSTREAM, Change Agent, Cities, Community, Design, East West Link, Landscape architecture, Moonee Ponds Creek, Place, Property development, RMIT, Spatial political economy, Strategy, Systems, Urban design, Urban planning, Urban politics


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text:
\section{Introduction}
\label{sec:intro}
A 2D image is the projection of an underlying 3D world, but we as humans have no trouble in understanding this structure and imagining how an image will look from other views. Consider the train shown in Figure~\ref{fig:vis_popup}, the reason we can seamlessly predict its other views just from a single image is because of the abstractions we have learned from past experience of watching several trains, or similar shaped objects from different views. Enabling machines with such an ability to reason about 3D from a single image will bring trillions of still photos to life with several applications in virtual reality, animation, image editing, and robotics.

This goal of synthesizing novel views from 2D images has been pursued for decades, from early efforts relying completely on geometry~\cite{debevec1996modeling,zitnick2004high,seitz2006comparison} to more recent learning based approaches~\cite{zhou2018stereo,srinivasan2019pushing,aliev2019neural,mildenhall2020nerf,liu2020neural,riegler2020free}. Over the years, significant progress has been made in this direction. However, although generating impressive photorealistic output views, most of these previous approaches require multiple images or ground-truth depth at test time, which severely hinders their practicality as a general tool.

On the other hand, the holy grail of developing a general-purpose tool to convert any arbitrary single image into a corresponding 3D structure for generating novel views has kept the community hooked for years. To compensate for the lack of multiple views or 3D models at test time, methods on single-image 3D rely on statistical learning from data.
This line of work can be traced back to classic works of Hoiem~\etal~\cite{hoiem2005automatic}, followed by Saxena~\etal~\cite{saxena2008make3d}, to obtain `qualitative 3D' from a single image by marrying geometry and learning to see images as a collection of planes.

An ideal approach to general-purpose view synthesis should not only rely on a single image at test time, but also learn from easy-to-collect supervision at training. In the deep learning era, there is growing interest in end-to-end methods with intermediate 3D representations supervised by multiple images and no explicit 3D information during training. However, they are mostly applied to objects~\cite{kulkarni2015deep,tatarchenko2016multi,worrall2017interpretable,kanazawa2018learning,sitzmann2019scene} and not scenes, and are either category-specific, applied to synthetic scenes, or both. Recent works~\cite{chen2019monocular,wiles2020synsin,tucker2020single} alleviate this issue by training with multiple views of real-world scenes, relying on point cloud or multiplane images as intermediate representations. However, multiplane images are mostly good for small view-point changes as each plane is at a constant depth, and for point cloud, one needs to represent each point in a scene individually, making it inefficient to scale to high-res data or large view-point changes.
In contrast, meshes provide a sparser scene representation, \eg, a single mesh face can theoretically represent the whole flat surface of a wall, making it ideal for single-image view synthesis. However, mesh recovery from single images has been studied mostly for object images and in a category-specific manner~\cite{blanz1999morphable,kanazawa2018learning,kulkarni2019canonical}.

In this paper, we present an end-to-end approach for novel view synthesis from \textit{a single image} via an intermediate \textit{mesh representation}. Unlike mesh reconstruction for objects of certain categories, generating meshes for a scene is challenging as there is no notion of mean or canonical shape to start from, or silhouette from segmentation for supervision. We side-step this problem by wrapping by a deformable mesh sheet over the 3D world -- much like wrapping a 2D tinfoil onto a 3D pan before baking! We name this shrink-wrapped mesh \textit{Worldsheet\xspace}, a term borrowed from physics for the 2D manifold of high-dimensional strings. After generating this Worldsheet\xspace for a given view, novel views are obtained by moving the camera in 3D geometry space (Figure~\ref{fig:method}), which allows us to train from just two views of a scene \textit{without any 3D or depth supervision}.

To train our model end-to-end, reconstructing mesh texture from inputs and rendering it in novel views both need to be differentiable. Latter is easily handled thanks to recent differentiable mesh renderers~\cite{kato2018neural,liu2019soft,ravi2020accelerating}. To address the former, we propose a differentiable texture sampler over projected 2D views, enabling gradient computation of the reconstructed texture map over the 3D mesh geometry.

Worldsheet\xspace allows generating novel views from a single image with large view-point changes. It consistently outperforms prior state-of-the-art by a significant margin on three benchmark datasets (Matterport \cite{chang2017matterport3d}, Replica \cite{straub2019replica}, and RealEstate10K \cite{zhou2018stereo}) and is applicable to very high-resolution images in-the-wild as shown in Figure~\ref{fig:vis_popup}.

\section{Related work}
\label{sec:related_work}

\begin{figure*}
\vspace{-1.5em}
\centering
\includegraphics[width=0.95\linewidth]{method.pdf}
\vspace{-0.5em}
\caption{An overview of our \textit{Worldsheet\xspace} approach. Given an input view $I_{in}$, we build a scene mesh by warping a $W_{m} \times H_{m}$ grid sheet onto the scene geometry via grid offset $(\Delta\hat{x}, \Delta\hat{y})$ and depth $z$ (Sec.~\ref{sec:method_mesh_pred}). Then, we sample the UV texture map $\hat{T}$ of the scene mesh differentiably (Sec.~\ref{sec:method_texture_sampling}) and render it from the target camera pose to output a novel view $I_{out}$. Our mesh warping is learned end-to-end using the losses on the novel view (Sec.~\ref{sec:method_analysis_by_synthesis}). We further apply an inpainting network over $I_{out}$ (not shown above; see Sec.~\ref{sec:method_inpainting}) to inpaint invisible regions and refine image details, outputting a refined novel view $I_{paint}$. We train with two views without any 3D or depth supervision and require just a single RGB image at test time.}
\label{fig:method}
\vspace{-1em}
\end{figure*}

\myparagraph{Novel view synthesis from multiple images} Traditionally, novel view synthesis used multiple input views at test time~\cite{chen1995quicktime,levoy1996light,gortler1996lumigraph}, and are often based on different representations. Among recent works, Waechter \etal \cite{waechter2014let} build scene meshes with diffuse appearance. StereoMag \cite{zhou2018stereo} proposes multiplane images (MPIs) from a stereo image pair as a layered scene representation. NPBG \cite{aliev2019neural} captures the scene as a point cloud with neural descriptors. NeRF \cite{mildenhall2020nerf} proposes a neural radiance field representation for scene appearance. NSVF \cite{liu2020neural} adopts sparse voxel octrees as scene representations. FVS \cite{riegler2020free} uses recurrent mapping and blending based on depth map of the scene from multi-view stereo. Yoon \etal \cite{yoon2020novel} combine depth from both single and multiple views to generate novel views of dynamic scenes. Access to multiple input views greatly simplifies the task, allowing the scene geometry to be recovered via multi-view stereo~\cite{seitz2006comparison}.

\myparagraph{Novel view synthesis from a single image}
In early works, Debevec \etal \cite{debevec1996modeling} recover 3D scene models and Horry \etal~\cite{horry1997tour} fit a regular mesh to generate novel views. Liebowitz \etal~\cite{liebowitz1999creating} and Criminsi \etal~\cite{criminisi2000single} generate meshes via projective geometry constraints. These methods came at the expense of manual editing until Hoiem \etal \cite{hoiem2005automatic} generate automatic 3D pop-up from 2D images based on vertical and ground planes. More recently in \cite{niklaus20193d,kopf2020one,shih20203d}, layered depth images are used for single image view synthesis based on a pretrained depth estimator. In \cite{tucker2020single}, online videos are used to train scale-invariant MPI representation for view synthesis. SynSin~\cite{wiles2020synsin} synthesizes novel views from a single image with a feature point cloud. We follow the same experimental setup as SynSin but learns to construct scene meshes instead of point clouds as scene representations to generate novel views from large viewpoint changes.

\myparagraph{Differentiable mesh rendering} Recent work on differentiable mesh renders \cite{kato2018neural,liu2019soft,ravi2020accelerating} allow learning 3D structures through synthesis. NMR \cite{kato2018neural} and SoftRas \cite{liu2019soft} reconstruct the 3D object shape as a mesh by rendering it, comparing it with the input image, and back-propagating losses to refine the mesh geometry. CMR \cite{kanazawa2018learning}, CSM \cite{kulkarni2019canonical} and U-CMR \cite{goel2020shape} build category-specific object meshes from images by deforming from a mean or template category shape through silhouette (and keypoints in \cite{kanazawa2018learning}) supervision.

Our method is aligned with the analysis-by-synthesis paradigm above. However, unlike most previous works that apply differentiable mesh rendering to objects, we learn the 3D geometry of \textit{scenes} through the rendering losses on the novel view. Moreover, instead of predicting a texture flow as in \cite{kanazawa2018learning,goel2020shape}, we propose to analytically sample the mesh texture from the input view with a differentiable texture sampler. Unlike \cite{pavlakos2019texturepose}, our differentiable texture sampler considers multiple mesh faces in the z-buffer (soft rasterization instead of only the closest one), and assumes perspective (instead of orthographic) camera projection.

\section{Worldsheet\xspace: Rendering the World in a Sheet}
In this work, we propose \textit{Worldsheet\xspace} to synthesize novel views from a single image, as shown in Figure~\ref{fig:method}. Given an input image $I_{in}$, we build a 3D scene mesh $M$ by warping a lattice grid (\ie a ``sheet'') onto the scene geometry through depth and grid offset, and sample the mesh's UV texture map in a differentiable manner. We then render the mesh from the target camera pose to output the novel view image $I_{out}$. We further apply an inpainting network over the rendered mesh to imagine the invisible regions, outputting a 2D image $I_{paint}$ as the final target view prediction. Our model is trained with only losses over the 2D output image of the target view, \textit{without any 3D or depth supervision}.

\subsection{Scene mesh prediction by warping a sheet}
\label{sec:method_mesh_pred}
From the input view image $I_{in}$ of size $W_{im} \times H_{im}$, we build a scene mesh by warping a $W_{m} \times H_{m}$ lattice grid (\ie a sheet) onto the scene, as shown in Figure~\ref{fig:method}. We first extract a $W_{m} \times H_{m}$ visual feature map $\{q_{w,h}\}$ from $I_{in}$ with a convolutional neural network.
Each $q_{w,h}$ is a feature vector at spatial location $(w,h)$ on the $W_{m} \times H_{m}$ network output. In our implementation, we use ResNet-50 \cite{he2016deep} (pretrained on ImageNet) with dilation \cite{yu2015multi} to output features $\{q_{w,h}\}$.

From each $q_{w,h}$ on the feature map, we predict the grid offset $\Delta \hat{x}_{w,h}$ and $\Delta \hat{y}_{w,h}$ to decide how much the vertex $(w,h)$ on the grid should move away from its anchor positions within the image plane (we output $\Delta \hat{x}_{w,h}$ and $\Delta \hat{y}_{w,h}$ in
NDC space \cite{shreiner2009opengl} between $-1$ to $1$). We also predict how far each vertex is from the camera, \ie its depth $z_{w,h}$. These values are predicted using learned linear mappings as
\begin{eqnarray}
\Delta \hat{x}_{w,h} &=& \tanh\left(W_1 q_{w,h} + b_1 \right) / (W_{m} - 1) \label{eqn:offset_x}\\
\Delta \hat{y}_{w,h} &=& \tanh\left(W_2 q_{w,h} + b_2 \right) / (H_{m} - 1) \label{eqn:offset_y} \\
z_{w,h} &=& g\left(W_3 q_{w,h} + b_3\right) \label{eqn:depth}
\end{eqnarray}
where division by $(W_{m} - 1)$ and $(H_{m} - 1)$ ensures that the vertices can only move within a certain range. $g(\cdot)$ is a scalar nonlinear function to scale the network prediction into depth values. We use $g(\psi) = \alpha_g / (\sigma(\psi) + \epsilon_g) + \beta_g$ in our implementation, where $\sigma(\cdot)$ is the sigmoid function and $\alpha_g$, $\beta_g$ and $\epsilon_g$ are fixed hyper-parameters.

\myparagraph{Building the 3D scene mesh} We first build the mesh vertices $\{V_{w,h}\}$ from the grid offset and depth as
\begin{equation}
\label{eqn:mesh}
    V_{w,h} =
                        \begin{bmatrix}
    z_{w,h} \cdot (\hat{x}_{w,h} + \Delta \hat{x}_{w,h}) \cdot \tan(\theta_F / 2) \\
    z_{w,h} \cdot (\hat{y}_{w,h} + \Delta \hat{y}_{w,h}) \cdot \tan(\theta_F / 2) \\
    z_{w,h}\\
    \end{bmatrix}
\end{equation}
for $w=1,\cdots,W_{m}$ and $h=1,\cdots,H_{m}$. Here $\theta_F$ is the camera field-of-view, and $\hat{x}_{w,h}$ and $\hat{y}_{w,h}$ are anchor positions on the grid equally spaced from $-1$ to $1$.

Then, we connect the mesh vertices $\{V_{w,h}\}$ along the edges on the grid to form mesh faces $\{F\}$ as shown in Figure~\ref{fig:method} and obtain a 3D mesh $M = (\{V_{w,h}\}, \{F\})$. A vertex in the mesh is connected to its 4 or 8 neighbours on the grid.

\myparagraph{Regularization} The predicted scene mesh is regularized with two losses. First, we apply an L2 regularization term $L_{g} = \sum_{w,h} \left(\Delta \hat{x}_{w,h}^2 + \Delta \hat{y}_{w,h}^2\right)$ to the grid offset to avoid unnecessary large grid deformations.
To encourage the mesh surface to be smooth unless it needs to bend to fit the scene geometry, we also apply a Laplacian term $L_{m} = \sum_{w,h} \left\| \sum_{(\bar{w},\bar{h}) \in N(w,h)} \left( V_{\bar{w},\bar{h}} - V_{w,h} \right) \right\|_1$ on the mesh vertices,
where $N(w,h)$ are the adjacent vertices to $(w, h)$.

\subsection{Differentiable texture sampler}  \label{sec:method_texture_sampling}

To render the mesh in another camera pose for novel view synthesis, we would like to reconstruct its texture from the scene appearance in the input view. While a few approaches \cite{kanazawa2018learning,goel2020shape} build mesh texture with a learned texture flow on objects, it is hard to apply the same to scenes, which are unlike birds or cats and do not have canonical shapes. In this work, we take an alternative route and propose a differentiable texture sampler.
We \textit{analytically} sample the mesh texture $\hat{T}$ as a UV texture map \cite{shreiner2009opengl} from the input view $I_{in}$,
where gradients $\partial \hat{T} / \partial V$ and $\partial \hat{T} / \partial I_{in}$ over the vertex coordinates and the input image can be computed.

To implement this texture sampler, we first build a z-buffer containing top $K$ mesh faces on each image pixel $(i, j)$ as in PyTorch3D \cite{ravi2020accelerating}, and splat the RGB pixel intensities from the image $I_{in}$ onto the UV texture map $\hat{T}$. Specifically, we compute the blending probability $p_{i, j}^{k}$ for the $k$-th covering mesh face (sorted in ascending z-order in the z-buffer) on pixel $(i, j)$ based on the softmax RGB blend in \cite{ravi2020accelerating} as $p_{i, j}^{k} = D_{i, j}^{k} \cdot \exp(-Z_{i, j}^{k}) / \sum_{k'} D_{i, j}^{k'} \cdot \exp(-Z_{i, j}^{k'})$, where $D_{i, j}^{k}$ is the soft-rasterization probability map \cite{liu2019soft} of the $k$-th face on the image (nearly 0 outside the face, nearly 1 inside the face, and blurry on the edges; see Figure~4 in Liu \etal~\cite{liu2019soft}), and $Z_{i, j}^{k}$ is the depth value of the 3D point on the $k$-th face covering pixel $(i, j)$ in the z-buffer.

We then decompose the input image $I_{in}$ into $K$ images $I^{k}_{in}$, where $I^{k}_{in}(i, j) = I_{in} \cdot p_{i, j}^{k}$, and splat the RGB pixel intensities from each $I^{k}_{in}$ to a texture map layer $\hat{T}^k$ as
\begin{equation}
    \hat{T}^k = \mathrm{splat}(I^{k}_{in}, f^k)
\end{equation}
where the flow $(u,v)=f^k(i, j)$ maps image coordinates $(i, j)$ to UV coordinates $(u,v)$ on the $k$-th mesh face in the z-buffer. Here $\mathrm{splat}$ is a differentiable splatting operation (based on \cite{niklaus2020softmax}; see supplemental for details) from the image space $I^{k}_{in}$ to the texture space $\hat{T}^k$.
Finally, we sum all the $K$ texture maps as the final UV texture map $\hat{T} = \sum_{k}\hat{T}^k$.

In summary, the image pixels are splatted onto the texture space through each face, and blended together to obtain the final texture map. The entire process is differentiable with respect to both $I_{in}$ and the mesh vertex coordinates $\{V\}$, as one can analytically compute $\partial \hat{T}^k/\partial I^k_{in}$, $\partial \hat{T}^k/\partial f^k$, $\partial f^k/\partial V$, $\partial D/\partial V$, and $\partial Z/\partial V$.

\subsection{Learning scene geometry by view synthesis}
\label{sec:method_analysis_by_synthesis}

To synthesize a novel view, we project the mesh vertex coordinates $\{V\}$ from the input camera pose $\theta_{in}$ to $\{V^{tgt}\}$ in the camera coordinate space of the target view-point $\theta_{tgt}$. Then, we render the mesh $M^{tgt} = (\{V^{tgt}\}, \{F\})$ in the target camera pose along with its texture map $\hat{T}$ to output a 2D image $I_{out}$ of size $W_{im} \times H_{im}$ as the target view:
\begin{equation}
\label{eqn:render_mesh}
    I_{out} = \mathrm{render\_mesh}(\{V^{tgt}\}, \{F\}, \hat{T}).
\end{equation}
We use the differentiable mesh renderer in \cite{ravi2020accelerating} so that we can compute the gradients $\partial I_{out} / \partial V^{tgt}$ and $\partial I_{out} / \partial \hat{T}$. Through mesh rendering, we also obtain a foreground mask $F_{out}$ with the same size as $I_{out}$, indicating which pixels in the rendered image $I_{out}$ are covered by the mesh and which pixels are from background color, as shown in Figure~\ref{fig:seen_vs_hallucinated} (b).

Our model is supervised with paired input and target views of a scene (along with their camera poses). We use a pixel L1 loss $L^{rgb}_{out} = \|I_{out} - I_{tgt}\|_1 / (W_{im} \cdot H_{im}) \label{eqn:loss_rgb}$ and a perceptual loss~\cite{wang2018high,wiles2020synsin} $L^{pc}_{out} = P(I_{out}, I_{tgt})$, where $I_{tgt}$ is the ground-truth target view image. The model then needs just a single image at test time.

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{seen_vs_hallucinated.pdf}
\vspace{-1.8em}
\caption{Parts of the target view (the gray area in b) are often invisible from the input and must be imagined based on prior knowledge. We make plausible predictions over the invisible regions with an inpaint network (c). However, this task is inherently uncertain (\eg one cannot be sure about the rightmost cabinet in d).}
\label{fig:seen_vs_hallucinated}
\vspace{-.7em}
\end{figure}

\subsection{Inpainting and image refinement}
\label{sec:method_inpainting}

\begin{table*}[t]
\vspace{-1em}
\footnotesize
\begin{center}
\begin{tabular}{clcccccccccccc}
\toprule
& &\multicolumn{9}{c}{Matterport \cite{chang2017matterport3d}} & \multicolumn{3}{c}{Replica \cite{straub2019replica}} \\
 \cmidrule(lr){3-11} \cmidrule(lr){12-14}
& & \multicolumn{3}{c}{PSNR $\uparrow$} & \multicolumn{3}{c}{SSIM $\uparrow$} & \multicolumn{3}{c}{Perc Sim $\downarrow$} & PSNR $\uparrow$ & SSIM $\uparrow$ & Perc Sim $\downarrow$ \\
\# & Method & Both & InVis & Vis & Both & InVis & Vis & Both & InVis & Vis & \\
\cmidrule(lr){1-2} \cmidrule(lr){3-5} \cmidrule(lr){6-8} \cmidrule(lr){9-11} \cmidrule(lr){12-14}
1 & Im2Im \cite{zhou2016view} & 15.87 & 16.20 & 15.97 & 0.53 & 0.60 & 0.48 & 2.99 & 0.58 & 2.05 & 17.42 & 0.66 & 2.29 \\
2 & Tatarchenko \etal \cite{tatarchenko2016multi} & 14.79 & 14.83 & 15.05 & 0.57 & 0.62 & 0.53 & 3.73 & 0.74 & 2.50 & 14.36 & 0.68 & 3.36 \\
3 & Vox \cite{sitzmann2019deepvoxels} w/ UNet & 18.52  & 17.85 & 19.05 & 0.57 & 0.57 & 0.57 & 2.98 & 0.77  & 1.96 & 18.69 & 0.71 & 2.68 \\
4 & Vox \cite{sitzmann2019deepvoxels} w/ ResNet & 20.62 & 19.64 & 21.22 & 0.70 & 0.69 & 0.68 & 1.97 & 0.47 & 1.19 & 19.77 & 0.75 & 2.24 \\
5 & SynSin \cite{wiles2020synsin} & 20.91 & 19.80 & 21.62 & 0.71 & 0.71 & 0.70 & 1.68 & 0.43 & 0.99 & 21.94 & 0.81 & 1.55 \\
\midrule
6 & ours w/o inpainting & -- & -- & 25.42 & -- & -- & 0.80 & -- & -- & 0.68 & -- & -- & -- \\
7 & ours & \textbf{24.67} & \textbf{22.90} & \textbf{26.00} & \textbf{0.82} & \textbf{0.77} & \textbf{0.82} & \textbf{1.05} & \textbf{0.35} & \textbf{0.54} & \textbf{23.51} & \textbf{0.85} & \textbf{1.32} \\
\bottomrule
\end{tabular}
\end{center}
\vspace{-1.5em}
\caption{{Novel View Synthesis}: Performance of our and previous approaches on the Matterport dataset and the Replica dataset. All models are trained on Matterport and evaluated on both datasets. See Sec.~\ref{sec:exp_mp3d_replica} for details.}
\label{tab:exp_mp3d_replica}
\vspace{-1em}
\end{table*}

The target view image consists of two parts: things that can be directly seen from the input view $I_{in}$, and things that need to be imagined based on our prior knowledge of the visual world, as illustrated in Figure~\ref{fig:seen_vs_hallucinated}. As our mesh warping and rendering procedure in Sec.~\ref{sec:method_mesh_pred}, \ref{sec:method_texture_sampling} and \ref{sec:method_analysis_by_synthesis} builds a pixel-to-pixel correspondence between the input and the target view, it only renders pixels that are \textit{visible} from the input view. To obtain a plausible imagination of the \textit{invisible} image regions, we apply an inpainting network $G$ on the rendered mesh $I_{out}$ to fill the missing regions and output a new image $I_{paint} = G(I_{out})$ as the final target view.

We build our inpainting network based on the generator in pix2pixHD \cite{wang2018high}, which translates a 4-channel input (the rendered mesh $I_{out}$ and its foreground mask $F_{out}$) into a 3-channel output image $I_{paint}$. Our inpainting network outputs an entire image -- it not only fills the invisible regions but also refines the image details in the visible regions.
We apply the same RGB pixel L1 loss $L^{rgb}_{paint}$ and perceptual loss $L^{pc}_{paint}$ as in Sec.~\ref{sec:method_analysis_by_synthesis} on the inpainting output $I_{paint}$.

\subsection{Training}
\label{sec:method_training}

During training, we train our model using the Adam optimizer \cite{kingma2014adam} with a weighted combination each loss as
    $L = \lambda_1 L^{rgb}_{out} + \lambda_2 L^{pc}_{out} + \lambda_3 L^{rgb}_{paint} + \lambda_4 L^{pc}_{paint} + \lambda_5 L_{g} + \lambda_6 L_{m} \nonumber$
with $\lambda_1 = \lambda_3 = 8$, $\lambda_2 = \lambda_4 = 2$, $\lambda_5 = 0.2$, and $\lambda_6 = 10^{-4}$. Our model is trained for a total of 50000 iterations with batch size $64$ and $10^{-4}$ learning rate.

We use a grid mesh with size $W_{m} \times H_{m} = 33 \times 33$ (and also $65 \times 65$ in Sec.~\ref{sec:exp_realestate10k}). Following SynSin \cite{wiles2020synsin}, we use $W_{im} \times H_{im} = 256 \times 256$ as the input and output image size. Our mesh implementation is based on PyTorch3D \cite{ravi2020accelerating}.

\section{Experiments}

\begin{figure*}[t]
\vspace{-1em}
\small
\centering
\begin{tabular}{ccc}
\includegraphics[height=210pt,trim=0 0 825pt 0, clip]{mp3d_vis.pdf} &
\includegraphics[height=210pt,trim=138pt 0 276pt 0, clip]{mp3d_vis.pdf} &
\includegraphics[height=210pt,trim=690pt 0 0 0, clip]{mp3d_vis.pdf} \\
\end{tabular}
\vspace{-5em}
\caption{Novel views from our and previous methods on the Matterport dataset (scene mesh shown in the last column). The first row have same view-point change as in \cite{wiles2020synsin} while the second row has $2\times$ larger camera angle change.}
\label{fig:vis_mp3d}
\end{figure*}

\begin{table*}[t]
\footnotesize
\begin{center}
\begin{tabular}{clcccccccccccc}
\toprule
& &\multicolumn{9}{c}{Matterport \cite{chang2017matterport3d} ($2\times$ cam. change)} & \multicolumn{3}{c}{Replica \cite{straub2019replica} ($2\times$ cam. change)} \\
 \cmidrule(lr){3-11} \cmidrule(lr){12-14}
& & \multicolumn{3}{c}{PSNR $\uparrow$} & \multicolumn{3}{c}{SSIM $\uparrow$} & \multicolumn{3}{c}{Perc Sim $\downarrow$} & PSNR $\uparrow$ & SSIM $\uparrow$ & Perc Sim $\downarrow$ \\
\# & Method & Both & InVis & Vis & Both & InVis & Vis & Both & InVis & Vis & \\
\cmidrule(lr){1-2} \cmidrule(lr){3-5} \cmidrule(lr){6-8} \cmidrule(lr){9-11} \cmidrule(lr){12-14}
1 & Im2Im \cite{zhou2016view} & 14.93 & 15.16 & 15.28 & 0.51 & 0.56 & 0.46 & 3.26 & 0.93 & 1.91 & 15.91 & 0.63 & 2.63 \\
2 & Tatarchenko \etal \cite{tatarchenko2016multi} & 14.71 & 14.77 & 15.08 & 0.56 & 0.61 & 0.52 & 3.74 & 1.04 & 2.14 & 14.19 & 0.68 & 3.37 \\
3 & SynSin \cite{wiles2020synsin} & 19.15 & 17.76 & 20.69 & 0.67 & 0.66 & 0.66 & 2.06 & 0.78 & 0.96 & 19.63 & 0.77 & 1.94 \\
\midrule
4 & ours w/o inpainting & -- & -- & 24.20 & -- & -- & 0.76 & -- & -- & 0.69 & -- & -- & -- \\
5 & ours & \textbf{22.62} & \textbf{20.89} & \textbf{24.76} & \textbf{0.77} & \textbf{0.72} & \textbf{0.77} & \textbf{1.41} & \textbf{0.63} & \textbf{0.56} & \textbf{21.12} & \textbf{0.81} & \textbf{1.70} \\
\bottomrule
\end{tabular}
\end{center}
\vspace{-1.5em}
\caption{{Novel View Synthesis}: Generalization performance of our and previous approaches to larger view-point changes on the Matterport dataset and the Replica dataset. All models are trained on the Matterport dataset and evaluated on both datasets with $2\times$ camera angle changes than in the training data. See Sec.~\ref{sec:exp_mp3d_replica} for details.}
\label{tab:exp_mp3d_replica_2x_cam}
\vspace{-1em}
\end{table*}

We evaluate our model on three datasets: Matterport \cite{chang2017matterport3d}, Replica~\cite{straub2019replica}, and RealEstate10K \cite{zhou2018stereo}, following the prior work's~\cite{wiles2020synsin} experimental setup and details. In addition, we analyze the advantanges and limitations of wrapping a mesh sheet by testing on in-the-wild high resolution images.

\subsection{Evaluation on Matterport and Replica}
\label{sec:exp_mp3d_replica}

We first train and evaluate our approach on the Matterport \cite{chang2017matterport3d} dataset, which contains 3D scans of homes. We load the Matterport dataset in the Habitat simulator \cite{savva2019habitat}, following the same training, validation, and test splits as in SynSin~\cite{wiles2020synsin}. During training, we supervise our model with paired 2D images of the input and the target views. We empirically find that it works slightly better to first train the scene mesh predictor (Sec.~\ref{sec:method_mesh_pred}) and then freeze the scene mesh to further train the inpainting network (Sec.~\ref{sec:method_inpainting}), rather than training both components jointly from scratch.

\myparagraph{Metrics} Following SynSin \cite{wiles2020synsin}, we evaluate the quality of our predicted novel view images $I_{paint}$ using three metrics: Peak Signal-to-Noise Ratio (\textbf{PSNR}; higher is better), Structural Similarity (\textbf{SSIM}; higher is better), and Perceptual Similarity distance (\textbf{Perc Sim}; lower is better). The Perc Sim metric is based on the convolutional feature distance between the prediction and the ground-truth, which is shown highly correlated with human judgement \cite{zhang2018unreasonable,wiles2020synsin}. Since only a part of the target view image can be seen from the input image as illustrated in Figure~\ref{fig:seen_vs_hallucinated}, we separately evaluate these metrics on visible regions (\textbf{Vis}, which can be seen from the input view), invisible regions (\textbf{InVis}, which cannot be seen but must be imagined), and the entire image (\textbf{Both}). Note that the visible region masks are obtained from the ground-truth scene geometry and camera frustum (available from the Habitat simulator) instead of predicted by our mesh, and are the same as in SynSin's evaluation.

\myparagraph{Baselines} we compare our method to several previous approaches: \textbf{Im2Im} \cite{zhou2016view} is an image-to-image translation method which predicts a appearance flow to warp an input view to the target view based on an input camera transformation. \textbf{Tatarchenko \etal} \cite{tatarchenko2016multi} is similar to Im2Im, but directly predicts the target view image instead of an appearance flow. \textbf{Vox w/ UNet} and \textbf{Vox w/ ResNet} are two variants of the deep voxel representation \cite{sitzmann2019deepvoxels} with different encoder-decoder architectures based on UNet or ResNet, implemented in \cite{wiles2020synsin}. \textbf{SynSin} \cite{wiles2020synsin} projects a dense feature point cloud (extracted from every image pixel) to the target camera pose and applies a refinement network on the point cloud projection to output the target view image. See Sec.~\ref{sec:related_work} for more discussions on SynSin.

We also evaluate the prediction of our model before inpainting (\ie directly using the mesh rendering output $I_{out}$ as the target view) to analyze how well our method performs with texture sampling and mesh rendering alone.

\myparagraph{Results} The results are shown in Table~\ref{tab:exp_mp3d_replica}. Even without inpainting, the mesh rendering output $I_{out}$ from our method already outperforms previous approaches by a large margin under all the three metrics on the visible regions. With the help of an inpainting network, our final output $I_{paint}$ has significantly higher performance than previous work on both invisible and visible regions, achieving a new state-of-the-art performance on this dataset. Figure~\ref{fig:vis_mp3d} shows view synthesis examples from our method and previous work on the Matterport dataset, where our method can paint things such as doorframe or sofa at more precise locations.

\begin{figure}[t]
\footnotesize
\centering
\includegraphics[width=\linewidth]{replica_1.png} \\
\begin{tabular}{cccc}
~~~~input view & ~~~GT target view & ~~prediction & pred. w/o inpainting \\
\end{tabular}
\vspace{-1.5em}
\caption{Generalization of our model (trained on Matterport) to the Replica dataset without retraining.}
\label{fig:vis_replica}
\vspace{-1em}
\end{figure}

\myparagraph{Generalization to the Replica dataset} Following \cite{wiles2020synsin}, we also evaluate how well our model generalizes to another scene dataset, Replica \cite{straub2019replica}, which contains high-quality laser scans of both homes and offices. We take our model trained on the Matterport dataset and directly evaluate on the Replica dataset without re-training. The results are shown in Table~\ref{tab:exp_mp3d_replica}, where all methods are trained and evaluated under the same setting. It can be seen that our method achieves noticeably better generalization to this dataset and outperforms previous approaches by a large margin. Figure~\ref{fig:vis_replica} shows view synthesis examples on the Replica dataset.

\myparagraph{Generalization to larger view-point changes} We further analyze how well our and previous approaches generalize to larger camera pose changes beyond their training data. In this analysis, we sample new input-target view pairs on the test scenes with $2\times$ larger camera angle changes than in the training data, and directly evaluate all approaches on these new view-points without retraining. The results are shown in Table~\ref{tab:exp_mp3d_replica_2x_cam}, where our method largely outperforms other approaches under all metrics. Figure~\ref{fig:vis_mp3d} (second row) shows an example under $2\times$ larger camera angle change.

\subsection{Evaluation on RealEstate10K}
\label{sec:exp_realestate10k}

\begin{figure*}[t]
\vspace{-1em}
\small
\centering
\begin{tabular}{ccc}
\includegraphics[height=210pt,trim=0 0 825pt 0, clip]{realestate10k_vis.pdf} &
\includegraphics[height=210pt,trim=138pt 0 276pt 0, clip]{realestate10k_vis.pdf} &
\includegraphics[height=210pt,trim=690pt 0 0 0, clip]{realestate10k_vis.pdf} \\
\end{tabular}
\vspace{-5em}
\caption{Novel views from our and previous methods on the RealEstate10K dataset (scene mesh shown in the last column).}
\label{fig:vis_realestate10k}
\vspace{-1em}
\end{figure*}

\begin{table}[t]
\footnotesize
\begin{center}
\begin{tabular}{clccc}
\toprule
\multicolumn{5}{c}{RealEstate10K \cite{zhou2018stereo}} \\
 \cmidrule(lr){1-5}
\# & Method & PSNR $\uparrow$ & SSIM $\uparrow$ & Perc Sim $\downarrow$ \\
\cmidrule(lr){1-2} \cmidrule(lr){3-3} \cmidrule(lr){4-4} \cmidrule(lr){5-5}
1 & Im2Im \cite{zhou2016view} & 17.05 & 0.56 & 2.19 \\
2 & Tatarchenko \etal \cite{tatarchenko2016multi} & 11.35 & 0.33 & 3.95 \\
3 & Vox \cite{sitzmann2019deepvoxels} w/ UNet & 17.31 & 0.53 & 2.30 \\
4 & Vox \cite{sitzmann2019deepvoxels} w/ ResNet & 21.88 & 0.71 & 1.30 \\
5 & 3DView (similar to \cite{3dview}) & 21.88 & 0.66 & 1.52 \\
6 & SynSin \cite{wiles2020synsin} & 22.31 & 0.74 & 1.18 \\
7 & SynSin\dag~\cite{wiles2020synsin} & 22.83 & 0.75 & 1.13 \\
8 & StereoMag\ddag~\cite{zhou2018stereo} & 25.34 & \textbf{0.82} & 1.19 \\
\midrule
9 & ours ($33\times 33$ mesh) & 26.24 & \textbf{0.82} & 0.83 \\
10 & ours ($65\times 65$ mesh) & \textbf{26.74} & \textbf{0.82} & \textbf{0.80} \\
\bottomrule
\end{tabular}
\end{center}
\vspace{-1.5em}
\caption{Results on the RealEstate10K dataset. We follow the evaluation protocol in SynSin and take the best metrics from predictions over two input views for the same target view. See Sec.~\ref{sec:exp_realestate10k} for details. (\dag: SynSin trained $5\times$ longer for 250k iterations; \ddag: unlike all other approaches, StereoMag takes two views as input to predict the target view.)}
\label{tab:realestate10k}
\vspace{-1em}
\end{table}

\begin{table*}[t]
\vspace{-1em}
\footnotesize
\begin{center}

\begin{tabular}{clccccccccc}
\toprule
& &\multicolumn{9}{c}{RealEstate10K \cite{zhou2018stereo} (averaged metrics over all predictions)} \\
 \cmidrule(lr){3-11}
& & \multicolumn{3}{c}{PSNR $\uparrow$} & \multicolumn{3}{c}{SSIM $\uparrow$} & \multicolumn{3}{c}{Perc Sim $\downarrow$} \\
\# & Method & Both & Peripheral & Center & Both & Peripheral & Center & Both & Peripheral & Center \\
\cmidrule(lr){1-2} \cmidrule(lr){3-5} \cmidrule(lr){6-8} \cmidrule(lr){9-11}
1 & Im2Im \cite{zhou2016view} & 15.56 & 15.65 & 15.80 & 0.51 & 0.53 & 0.44 & 2.59 & 1.93 & 0.65 \\
2 & Tatarchenko \etal \cite{tatarchenko2016multi} & 11.11 & 11.32 & 10.68 & 0.32 & 0.35 & 0.24 & 3.96 & 2.96 & 1.13 \\
3 & SynSin \cite{wiles2020synsin} & 20.11 & 20.02 & 21.15 & 0.67 & 0.67 & 0.67 & 1.52 & 1.18 & 0.31 \\
4 & SynSin\dag~\cite{wiles2020synsin} & 20.47 & 20.35 & 21.65 & 0.68 & 0.68 & 0.68 & 1.49 & 1.16 & 0.29 \\
\midrule
5 & ours ($33\times 33$ mesh) & 23.11 & 22.90 & 24.83 & \textbf{0.75} & 0.74 & \textbf{0.76} & 1.17 & 0.94 & \textbf{0.21} \\
6 & ours ($65\times 65$ mesh) & \textbf{23.41} & \textbf{23.20} & \textbf{25.17} & \textbf{0.75} & \textbf{0.75} & \textbf{0.76} & \textbf{1.14} & \textbf{0.92} & \textbf{0.21} \\
\bottomrule
\end{tabular}
\end{center}
\vspace{-1.5em}
\caption{Performance of our and previous approaches on RealEstate10K, with metrics averaged over all predictions. We evaluate on the entire $256 \times 256$ image (\textbf{both}), the \textbf{center} $128 \times 128$ crop (nearly always visible), and the remaining \textbf{peripheral} regions (containing most of the invisible regions). See Sec.~\ref{sec:exp_realestate10k} for details. (\dag: SynSin trained $5\times$ longer for 250k iterations.)}
\label{tab:realestate10k_details}
\end{table*}

The RealEstate10K dataset \cite{zhou2018stereo} is built upon video frames extracted from YouTube videos on houses. The input view and the target view are different frames sampled from the video within a time range, and the camera poses are estimated using Structure-from-Motion over the video.

On this dataset, we follow the experimental setup in SynSin \cite{wiles2020synsin} and use the same training, validation, and test data. In addition to using a $33 \times 33$ mesh, we also train our model with a higher resolution $W_{m} \times H_{m} = 65 \times 65$ mesh, which is initialized from a trained $33 \times 33$-mesh model with a new transposed convolution layer to upsample on the feature map $\{q_{w,h}\}$ in Sec.~\ref{sec:method_mesh_pred} to $65 \times 65$ spatial dimensions.

We compare our method to several previous approaches. In addition to the baselines in Sec.~\ref{sec:exp_mp3d_replica}, we also compared to two additional approaches. \textbf{3DView} is a system similar to the Facebook 3D Photo \cite{3dview} based on layered depth images, implemented in \cite{wiles2020synsin}. \textbf{StereoMag} \cite{zhou2018stereo} builds multiplane images (MPIs) from a stereo pair, using images from \textit{two} different views as input at test time. Except for StereoMag, all other methods use a single view at test time.

\begin{figure*}[t]
\small
\centering
\includegraphics[width=0.31\textwidth]{0020.pdf}~~~~~~~~
\includegraphics[width=0.31\textwidth]{0024.pdf}~~~~~~~~
\includegraphics[width=0.31\textwidth]{0011.pdf}
\vspace{-2em}
\caption{Squared error maps in target view from our method and SynSin \cite{wiles2020synsin} on the RealEstate10K dataset (darker is higher error). Our method paints things in the novel view at more precise locations, resulting in lower error and higher PSNR.}
\label{fig:vis_realestate10k_psnr}
\vspace{-1em}
\end{figure*}

\myparagraph{Results}
We follow the evaluation protocol of \cite{wiles2020synsin} on RealEstate10K. However, to compare with StereoMag \cite{zhou2018stereo} that uses two input views on this dataset, the best metrics of two views were reported for single-view methods in \cite{wiles2020synsin}. That is, make two separate predictions based on two different input views respectively for each target view at test time and the select the best metrics among the two predictions as the scores for this target view. We believe this metric does not fully capture the true performance of single-image view synthesis as it evaluates on two views at test time, but we report it for apples-to-apples comparison to baselines. Note that apart from Table~\ref{tab:realestate10k}, all other results (Table~\ref{tab:exp_mp3d_replica}, \ref{tab:exp_mp3d_replica_2x_cam}, \ref{tab:realestate10k_details}) are average single-view results. The results in Table~\ref{tab:realestate10k} show that our method achieves the highest performance under the evaluation protocol described above, outperforming previous approaches by a noticeable margin. Besides, a higher resolution $65 \times 65$ mesh gives a further performance boost.

\myparagraph{Detailed analyses} As discussed above, we further evaluate by our usual protocol of taking the average metrics over all predictions (which is used on Matterport and Replica in Sec.~\ref{sec:exp_mp3d_replica}), to measure how well the model does on average from a single input view. Apart from metrics over the entire image, we would also like to analyze how well each model does on rendering regions seen in the input view vs. invisible regions (where things must be imagined). However, we cannot compute the exact visibility map as there are no ground-truth geometry annotations on the RealEstate10K dataset. To get an approximation, we evaluate on the central $\frac{W_{im}}{2} \times \frac{H_{im}}{2} = 128 \times 128$ crop of the target image (\textbf{Center}, which is nearly always visible) and the rest of the image (\textbf{Peripheral}, containing most of the invisible regions).

These detailed comparisons are shown in Table~\ref{tab:realestate10k_details}. It can be seen that our method achieves the highest performance on both the center regions (which are mostly visible) and the peripheral regions, outperforms the previous single-view based approaches by a large margin. Figure~\ref{fig:vis_realestate10k} shows predicted novel views on this dataset. In addition, we visualize the pixel-wise squared error map on the prediction from our method and SynSin \cite{wiles2020synsin} in Figure~\ref{fig:vis_realestate10k_psnr}, where our method paints objects at more precise locations compared to SynSin, resulting in higher PSNR and better quality.

\subsection{Analysis: testing the limits of wrapping sheets}
\label{sec:anaylsis}
So far, we showed that the idea of wrapping a mesh sheet onto an image achieves strong performance across all benchmarks. But one might wonder how good is a planar sheet prior for novel view synthesis in any arbitrary images. To test the limits of wrapping a mesh \textit{sheet}, we test it over a large variety of images including outdoor scenes, outdoor objects, indoor scenes, indoor objects, and even artistic paintings. We analyze our underlying mesh data structure by pretraining depth~\cite{lasinger2019towards} to fill in the $z$ values, and examine how well it generates novel views in-the-wild. As shown in Figure~\ref{fig:vis_popup} (top row), although missing a few details (such as tree branches), a scene mesh sheet captures the geometric structures sufficient enough to render high-resolution (960$\times$960) photorealistic novel views even from very large viewpoint changes. Please see videos at \href{https://worldsheet.github.io}{\textcolor{black}{\texttt{worldsheet.github.io}}} for animation of continuously generated views. This result confirms that our mesh sheet data structure and warping procedure, despite being simple, are flexible enough to handle the variety of the visual world.

\myparagraph{Limitations and Future Work}
Although shrink-wrapping a mesh sheet onto images works well on a wide range of scenes, one limitation is that we currently do not cut the mesh on depth discontinuities, assuming that the foreground objects (\eg trees) are connected to the background (\eg sky), which can create artifacts near object boundaries. Figure~\ref{fig:popup_failure} shows failure cases due to depth discontinuity.

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{popup_failure.pdf}
\vspace{-2em}
\caption{Some failures of our approach due to depth discontinuity, where the foreground objects (trees or flower) are blurry at their boundaries. See Sec.~\ref{sec:anaylsis} for a discussion.}
\label{fig:popup_failure}
\vspace{-1em}
\end{figure}

\section{Conclusion}
In this work, we propose Worldsheet\xspace, which synthesizes novel views from a single image by shrink-wrapping the scene in a grid mesh. Our approach jointly learns the scene geometry and generates novel views through differentiable texture sampling and mesh rendering, supervised with only 2D images of the input and the target views. The approach is category-agnostic and end-to-end trainable resulting in state-of-the-art performance on single-image view synthesis across three datasets by a large margin. Furthermore, we show that our approach to wrap a sheet is flexible enough to handle the variety of the visual world.

\subsection*{Acknowledgements}
\noindent
We are grateful to Alyosha Efros, Alex Berg, Angjoo Kanazawa, Shubham Goel, Devi Parikh, Ross Girshick, Georgia Gkioxari, Justin Johnson, Nikhila Ravi, Brian Okorn and other colleagues at FAIR and CMU for fruitful discussions. This work was supported in part by DARPA Machine Common Sense grant.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


The phenomenally popular Garmin Forerunner 305 was a breakthrough in fitness gadget functionality. The new 310XT model takes the Garmin sport watch technology to a new level.
Resistance bands are a popular and affordable alternative to traditional weight training. Research shows that regular resistance band training strengthens and tones muscles while increasing bone mass.
This versatile home training set from Agile Fitness includes three color-coded bands of light, medium and heavy tension.
The Aerobics Step Platform for Wii Fit is a welcomed enhancement to the Wii Balance Board, intensifying and adding a new dimension to Wii Fit workouts.
Following the success of P90X, RevAbs and INSANITY, Beachbody has now released a new and improved version of one of their earliest programs, Slim In 6.
Last week the world's largest search engine began offering its satellite-powered mapping technology to cyclists and runners in 150 U.S cities.
The latest DVD from Jillian Micheals offers a fresh approach to Yoga by incorporating repetitions and circuit training with hard-core Yoga power pose sequences. It's unlike any other Yoga workout.
The BFMG10 Sportsman is a compact, no frills unit with impressive versatility for a home gym in the under $300 price range. It gets a marginal recommendation for those on a budget who don't need more than a 145 lb. weight stack.
A new study appearing in the Archives of Internal Medicine further proves that exercise is directly linked to emotional well-being.
The Nike+ Sport Band accurately tracks calorie expenditure, time, pace and distance travelled, storing up to 30 hours of data.
The function of this product is similar to the Nike+ iPod Kit but with a simple, no-frills design that doesn't talk or play music.


Rangers manager Walter Smith has said he has no intention of making wholesale changes for the visit to Ibrox of Inverness on Saturday.
Smith could be forgiven for deciding to protect some of his players ahead of Tuesday's tough-looking Uefa Champions League trip to Spain to take on Valencia.
Gers are on the crest of a wave following a 3-1 Old Firm win at Parkead last weekend to continue their 100 per cent start to the defence of their Scottish crown.
But, speaking ahead of the game against Inverness - the last team to defeat Rangers in the SPL at Ibrox, claiming a 1-0 win in March 2009 - Smith will resist calls to make changes.
He said: "We want to try to retain the championship if we can so the team we pick tomorrow will be as good a team as we can put out."
Smith added: "We've got a reasonable group now and we can maybe look at leaving one or two out over future weeks because there are a lot of midweek games to come, if you include the international.
"It will be a difficult spell for us between now and Christmas and we'll need to leave a player out now and again but it won't be in tomorrow's game, we will be looking to play as strong a team as possible."
Rangers (from): McGregor, Whittaker, Papac, Broadfoot, Weir, Bougherra, Davis, McCulloch, Edu, Naismith, Lafferty, Miller, Weiss, Foster, Fleck, Hutton, Little, Alexander.


text:
ACCEPTED

#### According to
The Catalogue of Life, 3rd January 2011

#### Published in
J. F. Royle, Ill. bot. Himal. Mts. 1(11):388. 1840

#### Original name
null

### Remarks
null

redpajama_set_name: RedPajamaGithub


text:
Q: JavaScript logical operators questions? I just don't understand how && and || work. I wrote up a small code to help myself out and it just doesn't make any sense to me. You would click a button and the startGame() would be called.
var startGame = function() {
var quizAnswers = {
    name: prompt("What is your name?").toUpperCase(),
    age: prompt("What is your age?"),
    snack: prompt("What is your favorite type of snack out of the following: ice cream, apple, chips, cookies?").toUpperCase()
};

quizAnswers.confirmAge = function () {
        while (isNaN(this.age) === true) {
           this.age = prompt("The age that you entered- " + this.age + " -is not a number. Please enter a number.");
        };
};

quizAnswers.confirmAge();

quizAnswers.confirmSnack = function () {
        while ((this.snack !== "ICE CREAM") && (this.snack !== "APPLE") && (this.snack !== "CHIPS") && (this.snack !== "COOKIES")) {
            this.snack = prompt("The snack you entered- " + this.snack + " -is unrecognized. Please enter: ice cream, apple, chips, or cookies.").toUpperCase();
        };
};

quizAnswers.confirmSnack();

It would get name, age, and favorite snack and then check to see if the age is a number and the snack entered is one of the listed options. After messing with the while loop in the confirmSnack function, I figured out how to make it work, which is displayed above. But why is it && and not ||. And is there a way to shorten it like:
while (this.snack !== ("ICE CREAM" && "APPLE" && "CHIPS" && "COOKIES")) {
    this.snack = prompt("The snack you entered- " + this.snack + " -is invalid. Please enter: ice cream, apple, chips, or cookies.").toUpperCase();
        };

So the questions are to explain why &&(and) is used instead of ||(or) and if there is a way to shorten this code so i don't have to enter "this.snack !==" four times. I'm not an expert so please try to keep it simple.

A: The && operator is working just fine. This is actually a question of logic, not javascript.
You are asking the question while the answer is different from ALL of the possible answers.
It could be rewritten with || as the follwing:
while (!(this.snack == "ICE CREAM" || this.snack == "APPLE" || this.snack == "CHIPS" || this.snack == "COOKIES"))

Notice the ! operator in the beginning.
A shorter form to write it would be:
answers = ["ICE CREAM", "APPLE", "CHIPS", "COOKIES"];
while (answers.indexOf(this.snack) < 0) { ... }

Here you define a list of possible answers and you want to accept, and check if the answer is among them.

A: The && and || operators compare boolean values. (Boolean means true/false). That means if you execute 5 == 5 && 6 + 1 == 7 the interpreter does the following things:

*

*Evaluate 5 == 5. The == operator returns true if both sides are equal (as you probably know). Since 5 == 5 is true, we look at the next value (if it were false, because of short circuit operators, it would return false immediately).

*Evaluate 6 + 1 == 7. This is also true, so the returned value is true.
The && operator does not compare regular values such as "ICE CREAM" (well it does, but it converts it into a boolean value).
Now let's look at the code you provided:
this.snack !== ("ICE CREAM" && "APPLE" && "CHIPS" && "COOKIES")
First, the javascript interpreter executes ("ICE CREAM" && "APPLE" && "CHIPS" && "COOKIES"). Since all of those values are true, the value of that expression is true. So this comparison is essentially checking this.snack !== true, which is not what you want.
To solve your problem, I would suggest using indexOf. This checks if an element is in an array and returns -1 if there is no element. For example:
var validSnacks = ["ICE CREAM", "APPLE", "CHIPS", "COOKIES"];
while (validSnacks.indexOf(this.snack) === -1) {
    // Do something
}

A: If you use or(||), if any of the sub-statements (e.g. this.snack !== "ICE CREAM") evaluate to true, the entire statement will be true, causing the prompt to be shown.
On the other hand, using and(&&), all of the sub-statements must be true for the entire statement to evaluate as true, which is the behaviour that you want - you only want the prompt to show if snack is not one of the 4 options.
There is no way to shorten the code in the way you suggest. One thing you could do would be to make an array with the 4 options in it and check if that array contains snack.

redpajama_set_name: RedPajamaStackExchange


Do you think you can change the world? One person can make an impact on how the rest of the world lives. What if that person is you?
The list of world-changers is endless. From Marie Curie to J.K. Rowling, and our favorite life-changer of all, the great Steve Jobs.
It's hard to believe it's only been since 1989 when Tim Berners-Lee kick-started the World Wide Web. It was 1977 when Paul Lauterbur and Peter Mansfield's (MRI) Magnetic Resonance Imaging invention enabled surgeons to see inside the body's soft organs without invasive surgery or X-rays.
Filmmaker George Lucas founded Industrial Light and Magic in 1975 to bring his vision of Star Wars to life. ILM went on to revolutionize special effects in the movies, changing the economics of the movie industry. And just imagine how different life would be without the invention of the birth control pill by Gregory Pincus, M.C. Chang, and John Rock, which the FDA approved in 1960.
These are the contemporary world-changers, but think of all those who came before them. How do you think they were able to achieve such remarkable results?
According to Admiral William McRaven, a Navy SEAL for thirty-six years, these world-changers have the same skills that he learned in SEAL training.
SEAL training is six tortuous months of long runs in soft sand, midnight swims in cold water, obstacle courses, never-ending calisthenics, days without sleep and always feeling cold, wet, and miserable.
The purpose of this training is to seek out those who can become leaders in an environment of constant stress chaos, failure, and hardships.
Every morning at bed inspection, instructors check to see if the covers are tight, the pillow centered right under the headboard and the extra blanket folded neatly at the foot of the bed.
As ridiculous as it seems, this simple mundane task gives you a sense of pride and encourages you to do another task, and many more. Making your bed proves that little things matter.
If you can't do the little things right, you will never do the big things right. Success requires discipline, no matter how small the task may seem. Start every day with a small act of self-discipline.
2. Find someone to help you paddle.
Every day the boat crew is instructed to paddle several miles down the coast. No matter how high the seas (10 feet!), every paddle must be synchronized perfectly with the count of the guide. Following the guide, every member must exert equal effort or the boat will be tossed back on the beach.
Everyone must paddle. If you want to get from the starting point to the destination, you need a strong, in sync, support system and a guide to lead the way. No one achieves success alone. Find a mentor, support system, and create a team that moves you towards your destination.
3. Measure a person by the size of their heart, not the size of their flippers.
Boat crews are determined by the size of the men in them. One crew was named the "munchkin crew," all under 5-foot-five. They ran faster, paddler harder, and out swam all the other boat crews, always reaching the shore before the big guys. Size doesn't matter, only your will to succeed. If you want to change the world, don't judge people.
4. Get over being a sugar cookie and keep moving forward.
The lesson learned is that no matter how well you perform or prepare, you would always end up as a sugar cookie. That's just the way life is sometimes. Acknowledge and accept that even when you put your best effort forward, unfortunate circumstances might arise. When you get knocked down, get up, brush off, and keep on moving in the direction of your goal.
5. Don't be afraid of the circus.
Every day there are long runs, long swims, obstacle courses, and hours of calisthenics. Each event has a time limit. Failing means an invitation to "the circus"- two extra hours of calisthenics. The sole purpose: to wear you down, break your spirit, and force you to quit.
Life is filled with circuses. You will fall, fail, and it will be painful and discouraging, testing you to the very core of your being. Expect failure; don't fear it.
6. Sometimes you have to slide down the obstacle course headfirst.
The most challenging obstacle is the slide for life- a three level 30-foot tower with a long 200-foot rope in between. The goal is to climb the tower, grab the rope, swing underneath it hand over hand until you reach the other side. The record time remained the same since 1977. Until one day, a student slid down headfirst. It was dangerous, but he plunged forward and beat the record by half the time. If you want to change the world; you have to go against the grain. Fall out of the herd. Be the first one who is brave enough to do something different.
7. Don't back down from the sharks.
Before the night swim, after a brief training session on all species of sharks, SEALs are taught about how to deal with sharks in the open ocean. They are taught that if a shark encircles you, you must stand your ground. Do not swim away and do not act afraid. And if by chance the shark is darting towards you hungry for a midnight snack, punch him in the snout. If you want to compete the swim, you must learn how to deal with the sharks. The dark waters in the river of success are filled with sharks. There are the people vying for your position or product. Business ventures can get nasty. Stand your ground. Do't run away and do not afraid, even when you are.
8. Be your very best in your darkest moment.
Training to conduct underwater attacks against enemy ships, Navy SEALs swim underwater for over two miles with only a depth gauge and a compass to reach their target. As they approach the ship, they go under the keel where the engine is deafening and it's pitch black. Every SEAL knows that in the darkest moment, you must remain calm, composed, using your tactical skills, physical power, and all of your inner strength to carry out the mission. There are many dark moments in life. You may not be able to see where you're going or how to get there. The only thing you have to depend on is yourself: your skills, power, and inner strength. Stay calm in the darkness.
9. Start singing when you're up to your neck in mud.
The ninth week is "Hell Week." Six days without sleep, under constant physical and mental harassment and one day in the Mud Flats. Imagine fifteen hours in the freezing cold, mud, howling wind, and instructors pressuring you to quit. Admiral McRaven relates a story about that week. He said that, like a scene from the Titanic, after eight hours of chattering teeth and shivering moans, with only heads visible in the mud, one voice started singing. Before long, one voice became two until everyone was singing. Just one person's voice, enthusiastically singing was enough to spread hope and maintain the entire team until morning. One person can change the world by giving others hope.
10. Don't ever ring the bell.
A SEAL candidate can quit at any time. All he has to do is ring the bell in the center of the compound. Simply ringing the bell frees you from waking up at 5:00 a.m., sharks, mud, and freezing waters. No one wants to ring the bell.
Changing the world, like Navy SEAL training, is not easy. You will be tested, discouraged, and pushed to your physical limits in the worst conditions imaginable.
If you think you've got what it takes to change the world, then know that life is not fair and you will fail often. Take risks. Find someone to help you through life. Respect everyone. Face the bullies. Stand up when times are the toughest. Stay calm in the darkest moments. Lift the downtrodden. Start the day with a task completed… and never, ever give up!


This chapter examines what knowledge is necessary to promote equity in education. It argues that effective innovation to educate all children requires innovation supported by contextualized transfer of educational policies and practices. Contextualized transfer is the process of adapting practices that have demonstrated effectiveness in one context to another while examining the way in which various policy interventions relate to policy outcomes across national contexts, analyzing the dependency of those relationships on context, and determining how differences in contexts might limit the transferability of policy effects. At the core of this concept is an understanding of quality education as the product of a system, rather than the product of a single policy intervention, where context is a core element of this system.


films in the pipeline in 2012 including 'Yellow', 'Just Like a Woman', 'Two Jacks' and 'Nous York'.
influence as an actress and model has permeated into an obsession with her eclectic fashion sense.
New York City in 2007 and had a branch in London's Notting Hill.
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text:
Q: My computer won't boot My computer won't boot from hard disk but it can boot from usb or dvd.
When I'm at the installer screen of Windows, it can't see my hard drive, but my BIOS displays it correctly. My computer is a new Dell 3400 workstation.

A: It sounds like Windows Setup doesn't have the driver for the disk controller. Here is a link to Dell's driver download page for your T3400.
Use another computer to put the driver on a USB disk, then when Windows Setup asks you to choose an installation drive, insert the USB disk and load the driver.  Then Setup should be able to find your disk!

A: A couple of things
If it's a new Dell workstation you should have the ability in the start up to do some kind of system restore
If you're trying to install a different OS than the one that came with the dell box. - I've had a problem like this in the past where the install cd didn't have the correct sata drivers. I had to go in to the bios and change a setting (I think for sata emulation)
Ak


Julian Alston Wins Multiple AAEA Awards for "Effects of U.S. Public Agricultural R&D on U.S. Obesity and its Social Costs"
Julian Alston, Joanna P. MacEwan, and Abigail M. Okrent won multiple awards at the 2017 AAEA (Agricultural and Applied Economics Association) Annual Meeting for their article, "Effects of U.S. Public Agricultural R&D on U.S. Obesity and its Social Costs." They won the Outstanding Applied Economic Perspectives & Policy Article Award and the Food Safety & Nutrition Section's Award for Best Paper in 2016.
How much has food abundance, attributable to U.S. public agricultural R&D, contributed to high and rising U.S. obesity rates? This paper investigates the effects of public investment in agricultural R&D on food prices, per capita calorie consumption, adult body weight, obesity, public healthcare expenditures related to obesity, and consumer welfare. The authors find that a 10% increase in the stream of annual U.S. public investment in agricultural R&D in the latter half of the twentieth century would have caused a modest increase in the average daily calorie consumption of American adults, resulting in small increases in public healthcare expenditures related to obesity. On the other hand, such an increase in spending would have generated very substantial consumer benefits, and net national benefits, given the very large benefit-cost ratios for agricultural R&D. This implies that current policy objectives of revising agricultural R&D priorities to pursue obesity objectives are likely to be comparatively unproductive and socially wasteful. Moreover, R&D lags of decades mean that such an approach would be totally ineffective in the immediate horizon.
Julian is a distinguished professor in the Department of Agricultural and Resource Economics (ARE) at the University of California, Davis. Joanna is a research economist with Precision Health Economics LLC in Oakland. Abigail is an economist with the United States Department of Agriculture (USDA) Economic Research Service in Washington, D.C. Joanna and Abigail received their doctoral degrees from UC Davis ARE.
Alston, J.M., J.P. MacEwan, and A.M. Okrent. "Effects of U.S. Public Agricultural R&D on U.S. Obesity and its Social Costs." Applied Economic Perspectives and Policy 38(3)(2016): 492-520.