text:
Tax Credit for Plug-in Electric Vehicles Still Available for Certain Models
By D'Angelo Gore
Dozens of electric cars and trucks still qualify for a federal tax credit of up to $7,500 for buyers purchasing new plug-in electric vehicles. President Joe Biden misleadingly claimed that the Trump administration allowed the tax credit to expire, which, so far, is only the case for purchases of models made by Tesla and General Motors.
Biden made the claim in Aug. 5 remarks in which he talked about furthering U.S. investments in "clean" vehicles. He correctly noted that the Trump administration had rolled back fuel efficiency standards that were implemented during the Obama administration. However, he also suggested that then-President Donald Trump had eliminated Obama-era incentives for taxpayers to buy more fuel-efficient vehicles.
"Despite bipartisan support for consumer incentives, they also let the federal tax credit expire, penalizing autoworkers who were, at the time, selling the most electric vehicles in the world — in the United States," Biden said.
We asked the White House which expired tax credit Biden was referring to, but our inquiry went unanswered. The president appears to have been talking about the Plug-In Electric Drive Vehicle tax credit — which was enacted in 2008 under then-President George W. Bush, expanded in 2009 under then-President Barack Obama, and still exists in 2021 for purchases of new qualifying vehicles.
Initially, the nonrefundable credit ranges from $2,500 to $7,500 per vehicle, depending on the capacity of the battery used to power the vehicle. The amount of the credit begins to phase out once a manufacturer has sold 200,000 qualifying vehicles in the U.S. The credit is fully phased out in the sixth quarter after the sales cutoff is reached.
As president, Trump did propose eliminating the tax credit in his budgets for fiscal years 2020 and 2021. His administration estimated that doing so would save the federal government more than $2.5 billion over 10 years.
But Congress didn't repeal the credit, as Trump wanted.
The Trump White House also was reportedly instrumental in having a provision extending the tax credit removed from a government spending bill in December 2019. The proposal, which was supported by a bipartisan group of lawmakers, would have authorized a $7,000 tax credit for an additional 400,000 vehicles per manufacturer.
At the time, the expansion would have particularly benefited Tesla and GM, which lobbied for its inclusion after those automakers had reached the 200,000-sales threshold in late 2018 and early 2019, respectively. Biden may have been thinking of those two companies when he said the Trump administration penalized "autoworkers who were, at the time, selling the most electric vehicles in the world — in the United States."
But, for now, those are the only manufacturers whose electric or plug-in hybrid vehicles are no longer eligible for the tax credits.
According to lists maintained by the IRS and the Department of Energy, purchases of vehicles from dozens of manufacturers still qualify — including models made by companies such as Ford, Mercedes-Benz and BMW, which are all still well short of the 200,000-sales cap.
Editor's note: FactCheck.org does not accept advertising. We rely on grants and individual donations from people like you. Please consider a donation. Credit card donations may be made through our "Donate" page. If you prefer to give by check, send to: FactCheck.org, Annenberg Public Policy Center, 202 S. 36th St., Philadelphia, PA 19104.
Plug-In Electric Drive Vehicle tax credit
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text:
Q: Hostpolicy.dll not found error when running .NET Core from Python i am trying to run a Net Core Application from python and i am getting this error message:
 error:"A fatal error was encountered. The library 'hostpolicy.dll' required to execute the application was not found in 'D:\\BenchmarkingApp\\BenchmarkingApp\\bin\\Debug\\netcoreapp2.1\\'.\r\nFailed to run as a self-contained app. If this should be a framework-dependent app, add the D:\\BenchmarkingApp\\BenchmarkingApp\\bin\\Debug\\netcoreapp2.1\\BenchmarkingApp.runtimeconfig.json file specifying the appropriate framework.\r\n"

Why do i get this error ?
Python code
import subprocess
csServerPath = "D:\\BenchmarkBuild\server\Server.dll"

def main():
    command = "dotnet D:\\BenchmarkingApp\\BenchmarkingApp\\bin\Debug\\netcoreapp2.1\\BenchmarkingApp.dll 1 2 3"
    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
    (output, err) = process.communicate()
    print("output:"+str(output))
    print("error:"+str(err))

main()

P.S If you point me towards this link : The library hostpolicy.dll was not found as duplicate, i have  the appname.runtimeconfig.json in the same path with the appname.dll

redpajama_set_name: RedPajamaStackExchange


text:
<?xml version="1.0"?>
<package>
  <name>riptide_launch</name>
  <version>0.0.15</version>
  <description>Description of OSU's Riptide AUV.</description>

  <maintainer email="cary.42@osu.edu">Conor Cary</maintainer>

  <license>BSD</license>

  <url type="website">http://go.osu.edu/uwrt</url>
  <url type="repository">http://github.com/osu-uwrt/riptide</url>

  <author email="cary.42@osu.edu">Conor Cary</author>

  <buildtool_depend>catkin</buildtool_depend>

  <build_depend>roslaunch</build_depend>

  <run_depend>roslaunch</run_depend>

  <export>
  </export>
</package>

redpajama_set_name: RedPajamaGithub


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text:
Liquid crystal wavefront corrector with modal response based on spreading of the electric field in a dielectric material
Mikhail Loktev, Gleb Vdovin, Nikolai Klimov, and Svetlana Kotova
Mikhail Loktev,1 Gleb Vdovin,1 Nikolai Klimov,2 and Svetlana Kotova3,4
1Electronic Instrumentation Laboratory, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands
2OKO Technologies, R̈ntgenweg 1, 2624 BD Delft, The Netherlands
3OOO "Konstanta", Podshipnikovaya st., 3 - 24, 443056 Samara, Russia
4P. N. Lebedev Physical Institute of the Russian Academy of Sciences, Samara branch Novo-Sadovaya 221, 443011 Samara, Russia
M Loktev
G Vdovin
N Klimov
S Kotova
Mikhail Loktev, Gleb Vdovin, Nikolai Klimov, and Svetlana Kotova, "Liquid crystal wavefront corrector with modal response based on spreading of the electric field in a dielectric material," Opt. Express 15, 2770-2778 (2007)
Beam steering
Deformable mirrors
Spatial frequency
Active or adaptive optics (010.1080)
Liquid-crystal devices (230.3720)
Spatial light modulators (230.6120)
Revised Manuscript: January 29, 2007
Manuscript Accepted: February 1, 2007
A novel liquid crystal (LC) wavefront corrector with smooth modal influence functions is proposed and realized. The device consists of a thin layer of planar aligned nematic LC sandwiched between a glass plate with a conductive electrode and a plate made of ceramic material with a very high dielectric constant. Control electrodes are positioned on the back side of the ceramic plate, opposite to the LC. The modal character of the response is determined by spreading of the electric field in the ceramic plate. The device implemented is operating in a reflective (mirror) mode; however, similar principles can be used to build a transmissive device. Low cost and simplicity of control make it a good alternative to continuous face-sheet deformable mirrors.
Modal interaction matrix measurement for liquid-crystal corrector: precision evaluation
Quanquan Mu, Zhaoliang Cao, Zenghui Peng, Yonggang Liu, Lifa Hu, Xinghai Lu, and Li Xuan
Opt. Express 17(11) 9330-9336 (2009)
Liquid-crystal adaptive lenses with modal control
A. F. Naumov, M. Yu. Loktev, I. R. Guralnik, and G. Vdovin
Opt. Lett. 23(13) 992-994 (1998)
Modal liquid crystal array of optical elements
J. F. Algorri, G. D. Love, and V. Urruchi
Electrically variable liquid crystal lens based on the dielectric dividing principle
Oleksandr Sova, Victor Reshetnyak, Tigran Galstian, and Karen Asatryan
Control optimization of spherical modal liquid crystal lenses
A. F. Naumov, G. D. Love, M. Yu. Loktev, and F. L. Vladimirov
Opt. Express 4(9) 344-352 (1999)
S. R. Restaino, "On the use of liquid crystal for adaptive optics," in Optical applications of liquid crystals, L. Vicari, ed. (Institute of Physics Publishing, Bristol, Philadelphia, 2003), p.128.
D. J. Cho, S. T. Thurman, J. T. Donner, and G. M. Morris, "Characteristics of a 128×128 liquid-crystal spatial light modulator for wave-front generation," Opt. Lett. 23,969–971 (1998).
D. Dayton, S. Browne, J. Gonglewski, and S. Restaino, "Characterization and control of a multi-element dual-frequency liquid-crystal device for high-speed adaptive optical wave-front correction," Appl. Opt. 40,2345–2355 (2001).
L. Hu, L. Xuan, Y. Liu, Z. Cao, D. Li, and Q. Mu, "Phase-only liquid-crystal spatial light modulator for wave-front correction with high precision," Opt. Express 12,6403–6409 (2004).
Holoeye Photonics AG, High-resolution LCoS Spatial Light Modulators, http://www.holoeye.com
N. Hashimoto, "Electro holography and active optics," in Optical applications of liquid crystals, L. Vicari, ed. (Institute of Physics Publishing, Bristol, Philadelphia, 2003), p.105.
A. F. Naumov, M. Y. Loktev, I. R. Guralnik, and G. V. Vdovin, "Liquid crystal adaptive lenses with modal control," Opt. Lett. 23,992–994 (1998).
T.-L. Kelly, A. F. Naumov, M. Y. Loktev, M. A. Rakhmatullin, and O. A. Zayakin, "Focusing of astigmatic laser diode beam by combination of adaptive liquid crystal lenses," Opt. Commun. 181,295–301 (2000).
G. Vdovin, M. Loktev, and A. Naumov, "On the possibility of intraocular adaptive optics," Opt. Express 11,810–817 (2003).
A. F. Naumov and G. Vdovin, "Multichannel liquid-crystal-based wave-front corrector with modal influence functions," Opt. Lett. 23,1550–1552 (1998).
S. P. Kotova, M. Y. Kvashnin, M. A. Rakhmatulin, O. A. Zayakin, I. R. Guralnik, N. A. Klimov, P. Clark, G. D. Love, A. F. Naumov, C. D. Saunter, M. Y. Loktev, G. V. Vdovin, and L. V. Toporkova, "Modal liquid crystal wavefront corrector," Opt. Express 10,1258–1272 (2002).
M. Loktev, G. Vdovin, and I. Guralnik, "Operation modes of a liquid-crystal modal wave-front corrector," Appl. Opt. 43,2209–2225 (2004).
OKO Technologies, FrontSurfer wavefront analysis and control software, http://www.okotech.com
G. D. Love, "Liquid crystal phase modulator for unpolarised light," Appl. Opt. 32,2222–2223 (1993).
V. G. Chigrinov, Liquid Crystal Devices: Physics and Applications (Artech House, Boston, 1999).
T. S. Chang and E. E. Loebner, "Crossover frequencies and turn-off time reduction scheme for twisted nematic liquid crystal displays," Appl. Phys. Lett. 25,1–2 (1974).
Browne, S.
Cao, Z.
Chang, T. S.
Chigrinov, V. G.
Cho, D. J.
Clark, P.
Dayton, D.
Donner, J. T.
Gonglewski, J.
Guralnik, I.
Guralnik, I. R.
Hashimoto, N.
Hu, L.
Kelly, T.-L.
Klimov, N. A.
Kotova, S. P.
Kvashnin, M. Y.
Li, D.
Liu, Y.
Loebner, E. E.
Loktev, M.
Loktev, M. Y.
Love, G. D.
Morris, G. M.
Mu, Q.
Naumov, A.
Naumov, A. F.
Rakhmatulin, M. A.
Rakhmatullin, M. A.
Restaino, S.
Restaino, S. R.
Saunter, C. D.
Thurman, S. T.
Toporkova, L. V.
Vdovin, G.
Vdovin, G. V.
Vicari, L.
Xuan, L.
Zayakin, O. A.
Appl. Opt. (3)
Appl. Phys. Lett. (1)
Opt. Commun. (1)
Fig. 1. Forming of an influence function in a piston-type (a) and modal-type (b) LC phase modulators.
Fig. 2. 3D distribution of the electric potential in the ceramic substrate with the circular actuator on the top and the LC layer on the bottom (a); maximum electric potential at the LC-ceramic interface vs actuator diameter (b); normalized distributions of the electric potential for different actuator diameters (c).
Fig. 3. Schematic diagram of a ceramic-based modal LC wavefront corrector and a control unit (a); layout and numbering of actuators for the 19-channel device (b).
Fig. 4. Influence function of the central actuator (a); simultaneous response of 2 (b), 4 (c), 7 (d) and 19 (e) actuators; amplitude is 110 V and frequency 1 kHz for all addressed actuators.
Fig. 5. Simultaneous response of two adjacent actuators: V 1=V 2=110 V (a); V 1=110 V, V 2=-110 V (b); other actuators are biased at Vi =22 V, i = 3…19, for both patterns.
Fig. 6. Simultaneous response of four actuators (central actuator encircled by three adjacent ones): V 1=V 2=V 4=V 6=110V (a); V 1=-110V, V 2=V 4=V 6=110V (b),(c); other actuators are off for both patterns. Interferometric patterns are shown in (a) and (b), and the result of the wavefront measurement with a Shack-Hartmann sensor is shown in (c).
Fig. 7. "Coherent" and "incoherent" composition of the influence functions of three actuators: amplitude 110 V and frequency 625 Hz for all three actuators (a); same amplitude, frequencies 625, 1250 and 2500 Hz (b).

redpajama_set_name: RedPajamaCommonCrawl


MISSISSAUGA, Ont. - Ontario's Liberals would refuse to strike any deals with their rivals in order to form a minority government if they fail to win a majority of seats in Thursday's election, Premier Dalton McGuinty said Sunday.
In a letter to Progressive Conservative Leader Tim Hudak, McGuinty ruled out forming a coalition with either the Tories or New Democrats.
"I am running to form a Liberal government – and only a Liberal government," he wrote.
"There will be no coalition with either your party or the NDP."
But he went even further when later pressed on whether he might work with the NDP in some capacity to form a minority Liberal government if needed.
"No coalition, no accord, no agreement, formal or informal, or any other linkage of any kind," McGuinty said.
Polls have indicated the Liberals and the Progressive Conservatives were in a dead heat heading into the final week of the campaign, with the New Democrats trailing but picking up support.
McGuinty refused to acknowledge even the possibility of a minority scenario _ or the need to work with rival parties — insisting the Liberals "won't have to worry about that."
In 1985, Ontario's the Liberals came to office as a minority government with support from the NDP in exchange for a pledge to pass certain legislation.
In recent days, Hudak has been accusing McGuinty of planning "backroom deals" with the New Democrats.
Earlier Sunday, he warned that Ontario could end up with the Liberals and NDP scheming together to hike taxes.
"The Liberals and the NDP: they'll increase taxes and they'll spend money on everything under the sun," Hudak said at a campaign stop.
When asked if he would work with the New Democrats if they dropped their plan to raise the corporate tax rate, Hudak indicated he wasn't interested.
"I'm in this election to win and bring change, not play 'Let's Make a Deal'," Hudak said.
NDP Leader Andrea Horwath was evasive when asked if she would rule out a coalition.
"Mr. McGuinty is busy talking about himself and his own issues; what the people of Ontario are interested in is what any government will do for them after Oct. 6."
Horwath talked up what an NDP government would do during its first 100 days in power, stressing priorities such as cutting the HST from hydro bills and freezing tuition.
While Horwath could hold the balance of power in a minority situation, the Liberals said McGuinty had addressed his letter only to Hudak because of the Tory leader's accusations of backroom dealings.
The premier also accused his opponents of using talk of a minority as political ammunition.
The strategies are reminiscent of Prime Minister Stephen Harper's, whose spring election included frequent warnings that the federal Liberals and NDP would scheme to form a coalition if his Conservatives did not get a majority.


text:
Q: Mongoose Schema pagination typing using mongoose-paginate-ts EDIT FIXED :
What I onited say in my initial post is that I was exporting the mongoose-pagination-ts function & interface from a global file, and then importing it in my schema files to use it. However, it appears that I need to import the module directly in each schema file in order for it to work, Interface cannot be export in a file and import in another, comming from a node_module in CommonJS (Apparently, not sure if it's the correct reason)

I'm facing the following issue :
The module mongoose-pagination-ts explains as follow :
import { mongoosePagination, Pagination } from "mongoose-paginate-ts";
type User = mongoose.Document & {
  username: String,
  accounts: [mongoose.Types.ObjectId]
};
const userSchema = new Schema({
  username: String,
  accounts: [{ type: ObjectId, ref: "Account" }]
});
userSchema.plugin(mongoosePagination);
const User: Pagination<User> = mongoose.model<User, Pagination<User>>("User", userSchema);

//User.paginate()

The build passes with no issue, but when I execute the server in development mode using the following command I have the bellow issue :
nodemon --ext 'ts,js,cjs,json' --ignore 'tests/' --exec 'ts-node-esm src/index.ts'
import { mongoosePagination, Pagination } from "mongoose-paginate-ts";
                             ^^^^^^^^^^
SyntaxError: Named export 'Pagination' not found. The requested module 'mongoose-paginate-ts' is a CommonJS module, which may not support all module.exports as named exports.
CommonJS modules can always be imported via the default export, for example using:
import pkg from 'mongoose-paginate-ts';
const { mongoosePagination, Pagination } = pkg;
    at ModuleJob._instantiate (node:internal/modules/esm/module_job:124:21)
    at async ModuleJob.run (node:internal/modules/esm/module_job:190:5)

tsconfig.js
{
  "compilerOptions": {
    "experimentalDecorators": true,
    "target": "ESNext" /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */,
    "module": "ESNext" /* Specify what module code is generated. */,
    "rootDir": "./src" /* Specify the root folder within your source files. */,
    "moduleResolution": "NodeNext" /* Specify how TypeScript looks up a file from a given module specifier. */,
    "baseUrl": "./src" /* Specify the base directory to resolve non-relative module names. */,
    "outDir": "build",
    "types": ["node", "mocha"] /* Specify type package names to be included without being referenced in a source file. */,
    "allowJs": true /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */,
    "checkJs": false /* Enable error reporting in type-checked JavaScript files. */,
    "esModuleInterop": true /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */,
    "forceConsistentCasingInFileNames": true /* Ensure that casing is correct in imports. */,
    "strict": false /* Enable all strict type-checking options. */,
    "skipLibCheck": true /* Skip type checking all .d.ts files. */,
    "resolveJsonModule": true
  },
  "ts-node": {
    "swc": true
  },
  "exclude": ["tests", "node_modules"]
}

Any solution is welcomed, thanks !
The error is clear : the module is exported as a CommonJS module and the proposed solution should work, however, the Pagination interface cannot be destructured as explained :
import pkg from 'mongoose-paginate-ts';
const { mongoosePagination, Pagination } = pkg; // Pagination is not found


text:
Commonwealth Games Scotland celebrates 50 years since Edinburgh 1970
By Ali Iveson
Commonwealth Games Scotland is celebrating Edinburgh 1970's golden jubilee online.
Commonwealth Games Federation leadership backs athlete activism on social issues
By Michael Pavitt
The Commonwealth Games Federation (CGF) has said it passionately believes that athlete advocacy and activism "humanises rather than politicises" sport amid the ongoing Black Lives Matter demonstrations.
CGF President Martin looks forward to "incredibly important" year
Commonwealth Games Federation (CGF) President Dame Louise Martin described 2019 as an "incredibly important year" for the organisation in her New Year's message.
Commonwealth Games Federation President made Dame by Queen
By Duncan Mackay
Commonwealth Games Federation (CGF) President Louise Martin has been made a Dame by The Queen's New Year's Honours List in the United Kingdom, it has been announced.
Belfast stripped of 2021 Commonwealth Youth Games
By Dan Palmer
Belfast has been stripped as host of the 2021 Commonwealth Youth Games with the race to hold the event now re-opened.
Creation of Commonwealth Sports Foundation will help development of smaller countries, claims CGF President
By Liam Morgan at the InterContinental Park Lane Hotel in London
The establishment of a Commonwealth Sports Foundation will help to continue development across the bloc after five new countries won Commonwealth Games medals at Gold Coast 2018, it was claimed here today.
Philip Barker: From Perth 1962 to Gold Coast 2018 for Commonwealth Games Federation President
By Philip Barker
Perth's expression of interest in staging the 2026 Commonwealth Games is certain to stir the memories of Louise Martin, who has just finished presiding over the 2018 Commonwealth Games in the Gold Coast.
Liam Morgan: CGF continuing to face battle for relevance as Gold Coast 2018 finally gets underway
By Liam Morgan at the Main Press Centre in Gold Coast
The Commonwealth Games Federation (CGF) may have portrayed Gold Coast 2018 as an event of firsts but it is an old topic which has been prominent in the build-up to and during the opening days of competition here.
Mike Rowbottom: "I now declare these Games...why won't this bloody thing…Open"
By Mike Rowbottom
"Very much connected." It was a key phrase in the Queen's Message delivered at the Opening Ceremony of the 2018 Commonwealth Games in the Gold Coast last night.
Kenya set to host 2019 Commonwealth Games Federation General Assembly
By Liam Morgan at the Sheraton Grand Mirage Resort in Gold Coast
Kenya is set to host next year's Commonwealth Games Federation (CGF) General Assembly pending a sign-off from the country's Government, it was announced here today.
Bid process for 2026 Commonwealth Games officially launched
By Michael Pavitt at the Sheraton Grand Mirage in Gold Coast
The Commonwealth Games Federation (CGF) have begun inviting expressions of interest for upcoming editions of the event, with the 2026 edition set to be awarded in September 2019.
Liam Morgan: Commonwealth Games Federation must avoid mistakes made by IOC as they head down double award path
Even the more cynical among us will reluctantly admit the International Olympic Committee (IOC) probably did the right thing when they awarded two editions of the Games at the same time.
Commonwealth Games Federation and ISSF hold meeting after shooting omitted from Birmingham 2022
Commonwealth Games Federation (CGF) President Louise Martin has revealed the organisation have met with the International Shooting Sport Federation (ISSF) following the decision not to include the sport on the programme for Birmingham 2022.
Commonwealth Games Federation President claims organisation setting the pace for equal gender representation in sport
Commonwealth Games Federation (CGF) President Louise Martin claims the organisation is setting the pace for equal gender representation in sport.
CGF leaders predict 2018 will be the Commonwealth's year
Louise Martin and David Grevemberg have claimed the Commonwealth has never been more relevant in a New Year's message, with the duo looking forward to Gold Coast 2018 and progress with Birmingham 2022.


Mimi Gray won the 13-15yrs age group Solo on Day Two of the ASA National Synchronised Swimming Championships 2016.
The Reading Royals swimmer executed an expressive routine to Andrea Bocelli's Nessun Dorma showed good technical ability and scored her a 66.4000 for a total of 132.2219.
"It feels really good to win gold. I've put so many hours into training and now it has all paid off. I train six or seven times a week, and usually it's for two or three hours at a time.
Second place went to Gray's club mate, Isobel Blinkhorn. Her rendition of Baila Conmigo by Juan Magan showed some nice changes in pace, and she made good use of the pool space to score 64.0333. She finished with an overall score of 126.2303.
The final place on the podium went to Millicent Costello of City of Leeds. Her expressive routine impressed the judges and the crowd alike. She showed great control throughout the routine with a well choreographed routine. The judges awarded her 61.6333 and she finished with a total of 125.2698.
Eleanor Blinkhorn of Reading Royals was the stand out performance from the 12yrs and under age group. It was a close call after the Figures competition, with all three of the top swimmers sitting between 64 and 65 points.
Blinkhorn scored over four points more than second place, taking gold with 126.8245 overall.
Second place went to Zoe Dimond from City of Bristol SC. She was only just behind Blinkhorn after the figures, and she finished with 122.3933.
The final spot on the podium went to Wallsall Synchro's Robyn Swatman, who took bronze with 122.3688.


text:
LEE, Edward Ivanhoe Woodhouse
(Service number 6/2184)
Sale, Victoria, Australia
Care of C. W. LEE, Dalgety and Co, Ashburton
2nd South Canterbury Regiment
C. W. LEE (father), care of Dalgety and Co, Ashburton
Height 5 feet 6 inches. Weight 130 lbs. Chest measurement 32-35 inches. Complexion brown. Eyes grey. Hair brown. Eyes both 6/6. Hearing good. Colour vision normal. Limbs well formed. Full and perfect movement of joints. Chest well formed. Heart and lungs normal. Teeth fair. Free from hernia, varicocele, varicose veins, haemorrhoids, inveterate or contagious skin disease. Vaccinated. Good bodily and mental health. No slight defects. .
5th Reinforcements
Canterbury Infantry Battalion
Maunganui or Tahiti or Aparima
Suez, Egypt
1914-15 Star; British War Medal; Victory Medal
11 August 1915 wounded at the Dardanelles.
New Zealand Ambulance Station, Sazli Beit Dere, Gallipoli, Turkey
Died of wounds received in action.
Chunuk Bair (New Zealand) Memorial, Chunuk Bair Cemetery, Gallipoli, Turkey
Memorial plaque, Bank of New Zealand (Officers of the BNZ who laid down their lives in the GreatWar 1914-1919), BNZ Arcade, Wellington; Ashburton War Memorial; St Stephen's Anglican Church, Ashburton, Roll of Honour
Born on 11 December 1896 at Sale, Victoria, Australia, Edward Ivanhoe Woodhouse LEE was the eldest son of Charles Woodhouse LEE and his wife Leah Lottie née FISHER. Edward was educated at Geraldine, Cheviot, and later at Waitaki Boys' High School. His father was a stock agent at Geraldine in the early 1900s.
Edward joined the Bank of New Zealand on 24 May 1913, and was employed at the Ashburton branch at the time of enlistment. He put his age up a couple of years to enlist and was one of the youngest soldiers to leave Ashburton. From the first announcement that New Zealand would send an Expeditionary Force to the war he was keen to go, but because he was under age he did not manage to get away until the Fifth Reinforcements left. He had served for two years as a high school senior cadet and already belonged to the 2nd South Canterbury Regiment. At this time his father was the Ashburton manager for Dalgety and Company.
Embarking on 13 June 1915 with the Canterbury Infantry Battalion, Edward arrived at Egypt on 24 July. Eighteen days later, on 11 August 1915, he was wounded at the Dardanelles and died of his wounds that very day at the New Zealand Ambulance Station, Sazli Beit Dere, Gallipoli, Turkey, aged 18 years. His name is recorded on the Chunuk Bair (New Zealand) Memorial, at the Chunuk Bair Cemetery, Gallipoli, Turkey.
By October 1915 more than thirty ex-pupils of Waitaki Boys' High School had given their lives, among them Edward Ivanhoe Woodhouse Lee, whose name was recorded on the school's Roll of Honour, with pride in the part Waitakians had "played in this titanic struggle". They, along with well over 200 on active service at this time, conveyed "very concrete and convincing instance of the effect of the imperialistic training that has characterised the school for so many years".
An Old Waitakians' Memorial Service of hymns, prayers and scripture readings was held on 4 April 1920 in the Opera House. Now the Roll of Honour was much longer - 118 old Waitakians had given their lives for God and Empire. Edward and many others were remembered and honoured by their Alma Mater.
Edward was a member of the Ashburton Old Boys' Football Club. He was held in high regard by those who knew him, and he was very popular among his fellows.
He was a member of St Stephen's Anglican Church, Ashburton, where a special service was held in commemoration of Anzac Day 1917. On this occasion a memorial tablet to the memory of fallen soldiers was unveiled. At a very impressive service in the church commemorating Armistice Day in 1920, a very large, handsome marble memorial tablet was unveiled and dedicated. It had been given to the church and contained the names of all those members of the parish who lost their lives during the Great War. Following a parade from the Drill Hall to the church, there were hymns, choral singing, prayers and scripture readings. The service closed with the sounding of the Last Post and Benediction.
A notice in the Ashburton Guardian of 11 August 1920 remembered Edward with love – "To memory ever dear." Edward's medals – 1914-15 Star, British War Medal and Victory Medal - were sent to his father in 1924 by which time he was living in St Albans, Christchurch.
Edward is remembered on the Bank of New Zealand Memorial plaque (Officers of the BNZ who laid down their lives in the Great War 1914-1919), BNZ Arcade, Wellington, on the Ashburton War Memorial, and on St Stephen's Anglican Church, Ashburton, Roll of Honour
Cenotaph Database [06 June 2015]; CWGC [06 June 2015]; NZ Defence Force Records (Archives NZ ref. AABK 18805 W5544 0067256) [05 & 18 June 2015]; CWGC [15 August 2013]; Sun, 7 September 1915, 27 April 1917, Ashburton Guardian, 7 September 1915, 26 April 1917, 11 August 1920, Press, 8 September 1915, 15 October 1915, 14 November 1927, Oamaru Mail, 8 September 1915, 19 October 1915, 5 April 1920, North Otago Times, 9 September 1915, Star, 20 September 1915, Free Lance, 1 October 1915 (Papers Past) [17 & 18 June 2015]; School Admission Records (Canterbury Branch NZSG) [05 June 2015]; NZ Electoral Rolls (ancestry.com.au) [17 June 2015]; Ashburton War Memorial image (nzhistory.net.nz) [17 June 2015]; Memorial Plaque, Bank of New Zealand Arcade, Wellington. (Photo Paul Hickford, 2014; AWMM [18 June 2015]; Maunganui Transport Voyages (www.flotilla-australia.com/hmnz.t) [18 June 2015]


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McOmber & McOmber, P.C.
Adrienne H McOmber
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Admitted to New Jersey and Federal District Court BarsMs. McOmber earned a Bachelor of Arts Degree from Cornell University where she was elected Class Vice President, President of the Prelaw Society, and was inducted into the Junior Women's Honorary Society. She attended Rutgers University School of Law in Newark and while a student married fellow student Richard D. McOmber. On the basis of scholastic achievement, she was selected and served as a Member of...
Admitted to New Jersey and Federal District Court Bars
Ms. McOmber earned a Bachelor of Arts Degree from Cornell University where she was elected Class Vice President, President of the Prelaw Society, and was inducted into the Junior Women's Honorary Society. She attended Rutgers University School of Law in Newark and while a student married fellow student Richard D. McOmber. On the basis of scholastic achievement, she was selected and served as a Member of the Rutgers Law Review.
Following law school, Ms. McOmber served as a Captain in the U.S. Army Judge Advocate General's Corps for three years in the Washington, D.C. area. She was assigned to Fort Myer, Virginia as a Legal Assistance Officer and subsequently to the Office of The Judge Advocate General in the Pentagon. She is a recipient of an Army Commendation Medal for Meritorious Service.
After returning to civilian life, Ms. McOmber started a law practice in Fair Haven, New Jersey as a sole practitioner specializing in matrimonial law until she and her husband joined together to form the law firm of McOmber & McOmber. She is the Managing Officer of the firm and practices in the areas of business, banking and finance, commercial and residential real estate, and wills and probate.
Ms. McOmber was active in the Monmouth Bar Association for many years and served as a Trustee of the Association as well as Chairperson of several committees including the Family Law Committee. She was the first woman elected to serve as President of the Monmouth Bar Association.
She is a member of St. Stepanos Armenian Apostolic Church in Elberon, New Jersey and is a Life Member and volunteer for the Holocaust, Genocide & Human Rights Education Center at Brookdale Community College, Lincroft, New Jersey. She is the mother of R. Armen McOmber, Esq. and Christian V. McOmber, Esq., both of the firm, and has four grandchildren.
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text:
\section{Introduction}

Multi-view stereo (MVS) aims to estimate the dense 3D model of the scene from a given set of calibrated images. Due to its wide applications in virtual/augmented reality and 3D printing and so on, much progress has been made in this domain~\cite{Furukawa2010Accurate,Strecha2006Combined,Merrell2007Real,Goesele2007Multi,Liu2009Continuous,Schonberger2016Pixelwise} in the last few years. However, recovering a dense and realist 3D model is still a challenging problem since the depth estimation in low-textured areas always fails.

The failure of depth estimation in low-textured areas mainly comes from the unreliable photometric consistency measure in these areas. As low-textured areas always appear in smooth homogeneous surfaces (Figure~\ref{fig:motivation}), many methods~\cite{Woodford2009Global,Gallup2010Piecewise} assume that these surfaces are piecewise planar. Then, they formulate this prior as a regularization term in a global energy framework to recover the depth estimation in low-textured areas. Due to the difficulty in solving such optimization problems, the efficiency of these methods is low and they are easy to be trapped in local optima. Recently, PatchMatch multi-view stereo methods~\cite{Zheng2014PatchMatch,Galliani2015Massively,Schonberger2016Pixelwise,Xu2018Multi} become popular as their used PatchMatch-based optimization~\cite{Barnes2009PatchMatch} makes depth map estimation efficient and accurate. As these methods do not explicitly model the planar priors, these methods still encounter the failure in low-textured areas. Based on the individual advantages of planar prior models and PatchMatch multi-view stereo, we expect to construct a planar prior assisted PatchMatch multi-view stereo framework to efficiently recover the depth estimation in low-textured areas.

\begin{figure}[t]
	\centering
	\subfigure[Reference image]{
		\includegraphics[width=0.45\linewidth]{fig1_DSC_0219.png}}
	\subfigure[Ground truth]{
		\includegraphics[width=0.45\linewidth]{fig1_ground_truth.png}}
	\subfigure[Triangulation]{
		\label{fig:subfig:motic}
		\includegraphics[width=0.45\linewidth]{fig1_Voronoi.png}}
	\subfigure[ACMP (Ours)]{
		\label{fig:subfig:motid}
		\includegraphics[width=0.45\linewidth]{fig1_ACMP.png}}
	\caption{Illustration of discrimination and depth maps obtained by our method. The piecewise planar priors (c) through triangulation can adaptively gain discrimination for different low-textured areas. This helps to obtain better depth estimation for large low-textured areas (d).}
	\label{fig:motivation}
\end{figure}

To embed the planar prior models into PatchMatch multi-view stereo, in this work we rethink the right way to build the multi-view aggregated matching cost and propose a planar prior assisted PatchMatch multi-view stereo framework to help the depth estimation in low-textured areas. In conventional PatchMatch multi-view stereo methods,  sparse credible correspondences can be distinguished in discriminative regions, such as edges and corners. As these correspondences always coincide with the vertices in the mesh representation of 3D models, this means the sparse credible correspondences almost constitute the skeleton of a 3D model. Therefore, we first triangulate these correspondences to produce planar models. Note that, the planar priors are also suited for non-planar regions as credible correspondences are very dense in these regions and can adaptively form triangular primitives of different sizes. To derive the planar prior assisted multi-view aggregated matching cost, we leverage a probabilistic graphical model to simultaneously model photometric consistency and planar compatibility. The planar compatibility constrains predicted depth estimates to fall within an appropriate depth range while the photometric consistency can better reflect the depth changes in well-textured areas. At last, to alleviate the influence of unreliable planar priors, multi-view geometric consistency is enforced to rectify erroneous depth estimates.

In a nutshell, our contributions are as follows: {\bf 1)} We propose a novel planar prior assisted PatchMatch multi-view stereo framework for multi-view depth map estimation. This framework not only inherits the high efficiency of PatchMatch multi-view stereo but also leverages planar priors to help the depth estimation in low-textured areas. {\bf 2)} We adopt a probabilistic graphical model to induce a novel multi-view aggregated matching cost. This novel cost function takes both photometric consistency and planar compatibility into consideration. We demonstrate the effectiveness of our method by yielding state-of-the-art dense 3D reconstructions on ETH3D benchmark~\cite{Schops2017Multi}. Our code will be available at \url{https://github.com/GhiXu/ACMP}.

\section{Related Work}

Our work is relevant to both PatchMatch multi-view stereo and planar priors, therefore we will review relevant literature in these areas.

\noindent\textbf{PatchMatch Multi-View Stereo} PatchMatch multi-view stereo methods exploit the core idea of PatchMatch~\cite{Barnes2009PatchMatch}, sampling and propagation , to effectively estimate depth maps for each image. Focusing on different problems, many PatchMatch multi-view stereo methods have been proposed. \cite{Zheng2014PatchMatch,Schonberger2016Pixelwise} jointly estimate depth maps and pixelwise view selection by a probabilistic graphical model. \cite{Galliani2015Massively} utilizes a diffusion-like propagation scheme to make better use of the parallelization of GPUs. By inheriting the checkerboard pattern of \cite{Galliani2015Massively}, ACMH~\cite{Xu2018Multi} designs an adaptive checkerboard sampling strategy to propagate more reliable hypotheses. Moreover, ACMH further exploits these hypotheses to infer pixelwise view selection. However, as the photometric consistency on which these methods depend cannot get reliable discrimination in low-textured areas, the depth estimation of these methods always fails in these areas. To get reliable discrimination from low-textured areas, \cite{Wei2014Multi} leverages multi-scale scheme to achieve this at low resolution images. Then, it propagates the discrimination to the original resolution images by considering the relative depth difference from all neighboring views. On the multi-scale scheme, ACMM~\cite{Xu2018Multi} further considers the influence of view selection and leverages multi-scale geometric consistency to propagate the discrimination. Additionally, \cite{Romanoni2019TAPA} extracts superpixels at two scales and constrains the hypotheses in low-textured areas by the planes fitted for each superpixel. However, the discrimination obtained by the multi-scale scheme sometimes is limited by the predefined scales especially for large low-textured areas. In contrast, we leverage piecewise planar priors built from triangulation to adaptively acquire the discrimination for different low-textured areas.

\noindent\textbf{Planar Priors} Planar prior models are popular in 3D reconstruction as many scenes can be represented by a variety of plane primitives, especially for man-made environments. \cite{Gennert1988Brightness,Woodford2009Global} formulate planar prior models as second-order smoothness priors in a global energy function framework. This leads to a triple clique representation in the global energy function, making the optimization very difficult. To distinguish planar regions and non-planar objects in urban scenes, \cite{Gallup2010Piecewise} train a planar classifier to obtain the raw segmentation results and combine these segments with multi-view photometric consistency to define a global energy function to refine the predictions. Besides, it is worth noting that there exist some methods~\cite{Geiger2010Efficient,Zhang2015MeshStereo} employing triangulation to construct planar priors for disparity estimation in stereo matching. \cite{Geiger2010Efficient} forms a triangulation on robustly matched correspondences to build a prior over the disparity space. This can not only reduce the disparity search space, but also recover low-textured surfaces. To simultaneously output a disparity map and a 3D triangulation mesh, \cite{Zhang2015MeshStereo} first partitions stereo images into 2D triangles with shared vertices. Then, it formulates a two-layer Markov random field to jointly model disparity maps and vertex splitting probabilities. This assumes that the scene structure is piecewise planar and imposes regularization in the region-based stereo. Similar to \cite{Geiger2010Efficient}, our method also leverages the triangulation to build piecewise planar priors. Differently, embedding the priors into multi-view stereo is nontrivial as it needs to consider the visibility and geometry constraints of different views. To this aim, we embrace planar priors with PatchMatch MVS to consider the visibility and geometric constraints of different views.

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.137\textwidth]{fig2_R1_DSC_0684.png}
	\includegraphics[width=0.137\textwidth]{fig2_R1_ground_truth.png}
	\includegraphics[width=0.137\textwidth]{fig2_R1_sparsedisp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R1_Voronoi.png}
	\includegraphics[width=0.137\textwidth]{fig2_R1_priordisp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R1_disp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R1_geodisp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_DSC_0634.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_ground_truth.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_sparsedisp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_Voronoi.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_priordisp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_disp.png}
	\includegraphics[width=0.137\textwidth]{fig2_R2_geodisp.png}
	\begin{tabular}{p{0.116\textwidth}<{\centering}p{0.116\textwidth}<{\centering}p{0.116\textwidth}<{\centering}p{0.116\textwidth}<{\centering}p{0.118\textwidth}<{\centering}p{0.118\textwidth}<{\centering}p{0.116\textwidth}<{\centering}}
		(a) & (b) & (c) & (d) & (e) & (f) & (g) \\
	\end{tabular}
	\caption{(a) images; (b) ground truth; (c) sparse correspondences; (d) triangulation; (e) planar model directly calculated from (d); (f) planar prior assisted PatchMatch MVS; (g) geometric consistency.}
	\label{fig:Pipeline}
\end{figure*}

\section{Planar Prior Assisted PatchMatch MVS}

Suppose we have a set of input images $\mathcal{I}=\{\emph{I}_{m}\,|\,m=1{\cdots}N\}$ with their corresponding camera parameters $\mathcal{P}=\{\emph{P}_{m}\,|\,m=1{\cdots}N\}$. Each image will be sequentially taken as a reference image $I_{\text{ref}}$ while the other images are source images $\mathcal{I}_{\text{src}}=\{\emph{I}_{j}\,|\,I_{j}\in\mathcal{I}\wedge I_{j}\neq I_{\text{ref}}\}$. Our work focuses on estimating the depth map of $I_{\text{ref}}$ in turn.

Currently, there exist two popular PatchMatch multi-view stereo frameworks, including sequential propagation pattern~\cite{Bailer2012Scale,Schonberger2016Pixelwise} and checkerboard propagation pattern~\cite{Galliani2015Massively,Xu2018Multi}. As pointed out in \cite{Xu2018Multi}, the latter one is more efficient and effective than the former one, thus we will build our algorithm on the checkerboard propagation pattern.

Different from the conventional PatchMatch multi-view stereo methods, our method also takes as input the sparse credible correspondences of the reference image . To estimate the depth information of low-textured areas, our method consists of two stage. In the first stage, sparse correspondences are generated by conventional PatchMatch MVS methods and thresholding. Then, we triangulate these correspondences to produce planar models. In the second stage, we jointly consider the previous obtained planar models and photometric consistency by constructing a probabilistic graphical model. This derives a novel multi-view aggregated matching cost. By embedding this novel cost to the pipeline of PatchMatch MVS, we can obtain good depth estimation for low-textured areas.

\subsection{Planar Model Construction}

To start our algorithm, we implement the method of \cite{Xu2018Multi} to obtain sparse credible correspondences. The method follows the pipeline of PatchMatch MVS, iteratively performing adaptive checkerboard sampling and propagation, hypothesis updating via multi-view aggregated photometric consistency cost and refinement. A depth estimate will be considered as a credible correspondence if its final cost is lower than $0.1$ (Figure~\ref{fig:Pipeline}c).

Given the sparse credible correspondences of $I_{\text{ref}}$, they always characterize the structure of a scene. Although the depth estimation for low-textured areas is lost, people can imagine the whole 3D model of a scene according to these correspondences. Based on this observation, we first triangulate these sparse credible correspondences to adaptively generate triangular primitives of different sizes. As can be seen from Figure~\ref{fig:Pipeline}d, the triangular primitives in well-textured areas are relatively small so that the structures of non-planar regions can be kept. On the other hand, the triangular primitives in low-textured areas are as large as possible to incorporate the information of credible correspondences.

For each triangular primitive, we use its corresponding three vertices to calculate its plane parameters in the coordinate of the reference camera, including depth information and normal information. The pixels inside the same triangular primitive share the same plane parameters. Figure~\ref{fig:Pipeline}e shows two examples of planar models. It can be observed that the priori plane parameters can almost coincide with the optimal estimates for low-textured areas. It is worth noting that the structures of thin objects are also described by the generated triangular primitives.

\subsection{Planar Prior Assistance}

With the priori plane hypotheses, the depth estimate for low-textured areas can be better approximated. However, these priori plane hypotheses also lead to many blocking artifacts in well-textured areas, especially in boundaries, whose depth information should be estimated well by photometric consistency. To take both photometric consistency and piecewise planar priors into consideration, we employ a probabilistic model to achieve this.

\noindent\textbf{Random Initialization} As a first step, we randomly generate a plane hypothesis $\theta_{l}=[d_{l},\boldsymbol{n}_{l}]$ for each pixel ${l}$ in the reference image, where $d_{l}$ is distance from a 3D plane to the origin and $\boldsymbol{n}_{l}$ is normal vector. We first calculate a matching cost for each source image via the plane hypothesis induced homography~\cite{Hartley2004Multiple}. Then, the initial multi-view aggregated matching cost for each hypothesis is computed by averaging the top-$K$ smallest matching costs.

\noindent\textbf{Hypothesis Sampling and Propagation} Following \cite{Galliani2015Massively,Xu2018Multi}, we divide all pixels in the reference image into a Red-Black pattern. This allows to use the hypotheses of red pixels to update those of black pixels and vice versa. Then, we use the adaptive checkerboard sampling of \cite{Xu2018Multi} to propagate eight neighboring good hypotheses to the current pixel to be estimated. These propagated hypotheses together with the current hypothesis $\theta_{0}$ of the pixel constitute the current candidate hypothesis set, $\boldsymbol{\theta}=\{\theta_{i}\,|\,i=0\cdots8\}$.

\noindent\textbf{Hypothesis Updating} In conventional PatchMatch multi-view stereo methods~\cite{Zheng2014PatchMatch,Schonberger2016Pixelwise,Xu2018Multi}, in order to determine the best hypothesis from the candidate hypothesis set, the following multi-view aggregated matching cost is defined by photometric consistency to measure the multi-view similarity,
\begin{equation}\label{eq:1}
c_{\text{photo}}(\theta_{i})=\frac{\sum_{j} w_{j}\cdot m_{i,j}}{\sum_{j} w_{j}},
\end{equation}
where $m_{i,j}$ is the matching cost between the reference patch and its corresponding source patch observed on source image $I_{j}$ via $\theta_{i}$ and $w_{j}$ is the view selection weight of $I_{j}$. As the photometric consistency is unreliable in low-textured areas, these methods always fail in these areas.

In contrast, given the planar priors as described before, we leverage a probabilistic graphical model to derive our novel multi-view aggregated matching cost. To construct the graphical model, we define the patch on pixel $l$ of $I_\text{ref}$ as $X^{\text{ref}}$. Also, the patches observed on all source images via $\theta_{i}$ are $\boldsymbol{X}_{i}^{\text{src}}$, the visibility information of all source images is assumed to be $\boldsymbol{Z}^{\text{src}}$ and the planar prior at pixel $l$ is $\theta_{\text{p}}=[d_{\text{p}},\boldsymbol{n}_{\text{p}}]$. Then, the graphical model of our approach is depicted in Figure~\ref{fig:PELAS}a. The joint probability is
\begin{equation}
P(\theta_{i},\boldsymbol{X}_{i}^{\text{src}},\boldsymbol{Z}^{\text{src}},\theta_{\text{p}}){\propto}P(\boldsymbol{X}_{i}^{\text{src}}|\theta_{i},\boldsymbol{Z}^{\text{src}})P(\theta_{i}|\theta_{\text{p}}).
\end{equation}
In this way, the maximum a-posteriori estimate of the plane hypothesis $\theta^{*}$ is given by
\begin{equation}\label{eq:3}
\theta^{*}=\displaystyle\mathop{\arg\max}P(\theta_{i}|\boldsymbol{X}_{i}^{\text{src}},\boldsymbol{Z}^{\text{src}},\theta_{\text{p}}).
\end{equation}
The above posterior can be factorized as
\begin{equation}\label{eq:4}
P(\theta_{i}|\boldsymbol{X}_{i}^{\text{src}},\boldsymbol{Z}^{\text{src}},\theta_{\text{p}}){\propto}P(\boldsymbol{X}_{i}^{\text{src}}|\theta_{i},\boldsymbol{Z}^{\text{src}})P(\theta_{i}|\theta_{\text{p}}).
\end{equation}

\begin{figure}[t]
	\centering
	\subfigure[]{
		\includegraphics[width=0.4\linewidth]{fig3_a.png}}
	\subfigure[]{
		\includegraphics[width=0.42\linewidth]{fig3_b.png}}
	\caption{(a) Graphical model of planar prior assistance. Given priori plane hypothesis $\theta_\text{p}$, the observation $\boldsymbol{X}_{i}^\text{src}$ on source images and the visibility information $\boldsymbol{Z}^\text{src}$, the optimal hypothesis $\theta^{*}$ is inferred. (b) Graphical model of view selection. At each iteration, given candidate hypotheses $\boldsymbol{\theta}$, the observation $\boldsymbol{X}^{j}$ corresponding to the hypotheses on source image $I_{j}$, and the visibility of neighboring pixels on source image $I_j$ is $\boldsymbol{Z}_{n}^{j}$, the visibility of pixel $l$ on source image $I_{j}$, $Z^{j}$, is inferred.}
	\label{fig:PELAS}
\end{figure}

Next, we define the likelihood function as follows,
\begin{equation}\label{eq:5}
P(\boldsymbol{X}_{i}^{\text{src}}|\theta_{i},\boldsymbol{Z}^{\text{src}})=e^{-\frac{c_{\text{photo}}(\theta_{i})^2}{\alpha}}.
\end{equation}
This function encodes the photometric consistency, making the low multi-view aggregated photometric consistency cost have high probability. It encourages our whole algorithm to choose the hypothesis with lower multi-view aggregated photometric consistency cost, which is consistent with the hypothesis update criteria in the conventional PatchMatch multi-view stereo methods. However, due to the unreliability of photometric consistency in low-textured areas, this likelihood function will not reflect hypothesis changes in these areas. In this case, it is important to leverage planar priors to reflect these changes. Thus, we define the planar prior as
\begin{equation}\label{eq:6}
P(\theta_{i}|\theta_{\text{p}})=\gamma+e^{-\frac{(d_{i}-d_{\text{p}})^2}{2\lambda_\text{d}}}\cdot e^{-\frac{{\arccos^2}\boldsymbol{n}_{i}^{\top}\boldsymbol{n}_{\text{p}}}{2\lambda_{\text{n}}}},
\end{equation}
where $\lambda_\text{d}$ is the bandwidth of depth difference and $\lambda_\text{n}$ is the bandwidth of normal difference. The planar prior encourages the propagated hypotheses to be close to the planar model at pixel $l$. We substitute Equation~(\ref{eq:4})-(\ref{eq:6}) into Equation~(\ref{eq:3}) and take the negative logarithm algorithm to get the following planar prior assisted multi-view aggregated matching cost
\begin{equation}\label{eq:7}
c_\text{p-photo}(\theta_{i})=\frac{c_\text{photo}(\theta_{i})^2}{\alpha}-\log[\gamma+e^{-\frac{(d_{i}-d_{\text{p}})^2}{2\lambda_\text{d}}}\cdot e^{-\frac{{\arccos^2}\boldsymbol{n}_{i}^{\top}\boldsymbol{n}_{\text{p}}}{2\lambda_{\text{n}}}}].
\end{equation}
Note that, the first term that encodes photometric consistency is the main component in the above equation. This means that the photometric consistency will change more obviously than the planar prior in well-textured areas. Moreover, when the photometric consistency cannot reflect hypothesis changes in low-textured areas, the planar prior will play a major role in the hypothesis updating.

As mentioned above, the photometric consistency is important to determine the depth information in well-textured areas. This can rectify the erroneous depth estimates induced by planar models in no-planar areas. According to Equation~(\ref{eq:1}), the reliability of multi-view aggregated photometric consistency depends upon the view selection weights. To calculate these weights, we design another probabilistic graphical model to make full use of the photometric consistency of different source images and the view selection information of neighboring pixels.

Specifically, we denote that the visibility of pixel $l$ on source image $I_{j}$ is $Z^{j}$, the visibility of neighboring pixels of pixel $l$ on source image $I_{j}$ is $\boldsymbol{Z}_{n}^{j}$, the candidate hypotheses are $\boldsymbol{\theta}$, the patch on pixel $l$ in the reference image is $X^\text{ref}$, and its corresponding patches observed on source image $I_{j}$ via $\boldsymbol{\theta}$ are $\boldsymbol{X}^{j}=\{X_{i}^{j}|i=0\cdots8\}$. The graphical model is depicted in Figure~\ref{fig:PELAS}b. According to the states of neighboring pixels,
the joint probability is
\begin{equation}
P(\boldsymbol{X}^{j},Z^{j},\boldsymbol{\theta},\boldsymbol{Z}_{n}^{j}){\propto}P(\boldsymbol{X}^{j}|Z^{j},\boldsymbol{\theta})P(Z_{l}^{j}|\boldsymbol{Z}_{n}^{j}),
\end{equation}
where $P(\boldsymbol{X}^{j}|Z^{j},\boldsymbol{\theta})=\sum_{i=0}^{8}P(X_{i}^{j}|Z^{j},\theta_{i})$ independently models the possible source image subset for each hypothesis while $P(Z^{j}|\boldsymbol{Z}_{n}^{j})=\sum_{l'\in \mathcal{N}(l)} P(Z^{j}|Z_{l'}^{j})$ models the smoothness of the view selection of neighboring pixels. Note that, $\mathcal{N}(l)$ stands for the four neighboring pixels of pixel $l$. Specifically, we define $P(X_{i}^{j}|Z^{j},\theta_{i})$ as
\begin{equation}
P(X_{i}^{j}|Z^{j},\theta_{i})=e^{-\frac{m_{i,j}^2}{2\sigma^2}},
\end{equation}
where $\sigma$ is a constant. And, $P(Z^{j}|Z_{l'}^{j})$ is defined as
\begin{equation}
P(Z^{j}|Z_{l'}^{j})=\left\{ \begin{array}{ll}
\eta, & \textrm{if}\;Z_{l}^{j}=Z_{l'}^{j}; \\
1-\eta, & {\textrm{else.}}
\end{array} \right.
\end{equation}

According to Bayesian rule, the view selection probability of pixel $l$ on source image $I_{j}$ is
\begin{equation}
P(Z^{j}|\boldsymbol{X}^{j},\boldsymbol{\theta},\boldsymbol{Z}_{n}^{j}){\propto}P(\boldsymbol{X}^{j}|Z^{j},\boldsymbol{\theta})P(Z^{j}|\boldsymbol{Z}_{n}^{j}).
\end{equation}
Based on the above view selection probabilities, we employ the Monte-Carlo sampling~\cite{Bishop2006PRML} to define the weight for each source image $I_{j}$ as $w_{j}$.

\noindent\textbf{Refinement} After each hypothesis updating, we refine the current selected hypothesis $\theta_{\text{c}}=[d_\text{c},\boldsymbol{n}_\text{c}]$ by generating extra candidate hypothesis set. Following \cite{Schonberger2016Pixelwise,Xu2018Multi}, this candidate hypothesis set is defined as
\begin{equation}\label{eq:12}
\{[d_\text{p},\boldsymbol{n}_\text{c}],[d_\text{r},\boldsymbol{n}_\text{c}],[d_\text{c},\boldsymbol{n}_\text{p}],[d_\text{c},\boldsymbol{n}_\text{r}],[d_\text{r},\boldsymbol{n}_\text{r}],[d_\text{p},\boldsymbol{n}_\text{p}]\},
\end{equation}
where $d_\text{p}$ and $\boldsymbol{n}_\text{p}$ are the randomly perturbed depth and normal with respect to $\theta_\text{c}$, $d_\text{r}$ and $\boldsymbol{n}_\text{r}$ are randomly generated depth and normal. If the cost of a new hypothesis is less than that of the current hypothesis, we will set it as the current hypothesis. The above hypothesis sampling and propagation, hypothesis updating and refinement are performed iteratively to produce the depth map for the reference image.

\subsection{Geometric Consistency}

In the previous Section, we consider both piecewise planar priors and photometric consistency to get better depth estimation in low-textured and well-textured areas. However, there still exist some errors. This attributes to some unreliable planar priors caused by some intractable erroneous sparse correspondences. To tackle these errors, we resort to multi-view geometric consistency~\cite{Schonberger2016Pixelwise,Xu2018Multi},
which is defined as
\begin{equation}\label{eq:13}
c_\text{geo}(\theta_{i})=\frac{\sum_{j}w_{j}\cdot (m_{i,j}+\lambda_\text{geo}\cdot \displaystyle\mathop{\min}(\Delta e_{j}(\theta_{i}),\tau_\text{geo}))}{\sum_{j}w_{j}},
\end{equation}
where $\lambda_\text{geo}$ is a geometric consistency regularizer, $\Delta{e}_{j}(\theta_{i})$ is the reprojection error between $I_\text{ref}$ and $I_{j}$ induced by $\theta_{i}$, and $\tau_\text{geo}$ is a truncation threshold to robustify the reprojection error against occlusions.

\begin{algorithm}[t]
	\caption{Planar Prior Assisted PatchMatch MVS}
	\label{alg:Overview}
	{\bf Input:} multi-view images with their camera parameters \\
	{\bf Output:} hypothesis maps
	\begin{algorithmic}[1]
		\FOR{each image}
		\STATE set reference image and source images
		\STATE randomly initialize a hypothesis map
		\FOR{iteration $i=1$ to $T_\text{photo}$}
		\STATE hypothesis sampling and propagation
		\STATE update the hypothesis map via Equation~(\ref{eq:1})
		\STATE refinement via Equation~(\ref{eq:12})
		\ENDFOR
		\ENDFOR
		\STATE sparse credible correspondences selection
		\STATE triangulation and generate planar models
		\FOR{each image}
		\STATE set reference image and source images
		\STATE randomly initialize a hypothesis map
		\FOR{iteration $i=1$ to $T_\text{p-photo}$}
		\STATE hypothesis sampling and propagation
		\STATE update the hypothesis map via Equation~(\ref{eq:7})
		\STATE refinement via Equation~(\ref{eq:12})
		\ENDFOR
		\ENDFOR
		\STATE use the hypothesis maps obtained above as extra input
		\FOR{each image}
		\STATE set reference image and source images
		\STATE use the previous obtained hypothesis map for the reference image as initialization
		\FOR{iteration $i=1$ to $T_\text{geo}$}
		\STATE hypothesis sampling and propagation
		\STATE update the hypothesis map via Equation~(\ref{eq:13})
		\STATE refinement via via Equation~(\ref{eq:12})
		\ENDFOR
		\ENDFOR
	\end{algorithmic}
\end{algorithm}

\subsection{The Algorithm}

The overall pipeline of our algorithm is summarized in Algorithm~\ref{alg:Overview}. From step 1 to step 9, we generate initial depth maps via conventional multi-view aggregated photometric consistency cost. Then, in step 10 and step 11, we select credible correspondences and triangulate them to generate planar models. From step 12 to step 20, we generate plane-awareness depth maps by our proposed planar prior assisted multi-view aggregated matching cost. Then, these depth maps are used as additional input in step 21. We further optimize these depth maps via geometric consistency from step 22 to step 30. To make each PatchMatch MVS process converge, $T_\text{photo}$, $T_\text{p-photo}$ and $T_\text{geoo}$ are set to 3, 3, 2, respectively.

\subsection{Fusion}

The depth maps estimated for individual images always contain noise and outliers. We follow the conventional PatchMatch pipeline~\cite{Schonberger2016Pixelwise,Xu2018Multi} to use a fusion step to produce the final point cloud. Each image is treated as the reference image in turn and its depth estimates are unprojected to the world coordinate to obtain 3D points. These 3D points are further projected to neighboring images to calculate their projected depths, normals and image coordinates. According to the estimated depths and normals in projected image coordinates, a consistent estimate is determined if its relative depth difference is lower than $0.01$, normal difference is lower than $10^\circ$, and reprojection error is less than $2$ pixels. The estimate that has two consistent neighboring estimates are kept and their unprojected 3D points are averaged to produce the final 3D point.

\begin{table*}[t]
	\caption{Percentage of pixels with absolute errors below $2cm$ and $10cm$ on the high-resolution multi-view training datasets of ETH3D benchmark (in $\%$). ACMP$\backslash$G means ACMP without geometric consistency. The related values are from \cite{Xu2018Multi}. The best results are marked in bold.}
	\centering

	\footnotesize

	\begin{tabular}{p{0.6cm}<{\centering}p{1.36cm}<{\centering}p{0.5cm}<{\centering}p{0.5cm}<{\centering}p{0.5cm}<{\centering}p{0.5cm}<{\centering}p{0.45cm}<{\centering}p{0.5cm}<{\centering}p{0.5cm}<{\centering}p{0.5cm}<{\centering}p{0.7cm}<{\centering}p{0.5cm}<{\centering}p{0.5cm}<{\centering}p{0.6cm}<{\centering}p{0.7cm}<{\centering}p{0.7cm}<{\centering}}
		\toprule
		\multirow{2}{*}{error} & \multirow{2}{*}{method} & \multirow{2}{*}{Ave.} & \multicolumn{7}{c}{indoor} & \multicolumn{6}{c}{outdoor} \\
		\cmidrule(lr){4-10} \cmidrule(lr){11-16}
		& & & deli. & kick. & offi. & pipes & relief & relief. & terrai. & courty. & elec. & faca. & mead. & playgr. & terrace \\
		\midrule
		\multirow{6}{*}{$2 cm$} & {COLMAP} & 65.0 & 69.7 & {43.5} & 26.3 & 41.1 & 86.3 & 85.8 & 57.6 & {82.6} & 71.0 & {74.2} & 54.6 & 70.9 & 80.8 \\
		& {openMVS} & 55.2 & 63.9 & 36.9 & 29.3 & 31.8 & 80.1 & 81.5 & 57.9 & 64.3 & 55.6 & 55.4 & 29.8 & 57.9 & 73.6 \\

		& {ACMH} & 68.5 & {73.3} & 42.7 & {32.3} & {53.6} & {89.1} & {90.3} & {71.4} & 79.9 & {74.8} & 68.5 & {57.1} & {75.3} & {82.0} \\

		& {ACMM} & 80.5 & {77.7} & {\bf 66.7} & {51.2} & {76.5} & {\bf 96.0} & {95.7} & {85.4} & {84.4} & {86.8} & {74.5} & {\bf 77.1} & {\bf 84.3} & {89.7} \\

		& {ACMP$\backslash$G} & 72.9 & 76.9 & 47.4 & 48.6 & 65.3 & 91.3 & 92.6 & 79.1 & 78.8 & 79.2 & 68.9 & 59.5 & 73.7 & 85.9 \\
		& {ACMP} & {\bf 81.9} & {\bf 81.9} & 62.0 & {\bf 65.6} & {\bf 78.6} & 94.8 & {\bf 95.8} & {\bf 88.4} & {\bf 84.5} & {\bf 88.7} & {\bf 76.6} & 76.8 & 80.6 & {\bf 90.7} \\
		\midrule
		\multirow{6}{*}{$10 cm$} & {COLMAP} & 73.7 & 80.6 & 51.4 & 34.2 & 47.8 & 89.6 & 89.3 & 63.5 & 93.4 & 77.4 & {90.9} & 70.1 & 81.0 & 89.1 \\
		& {openMVS} & 66.5 & 79.1 & 45.1 & 38.2 & 42.1 & 84.1 & 86.0 & 67.1 & 79.1 & 64.8 & 77.4 & 48.4 & 68.7 & 83.8 \\

		& {ACMH} & 79.1 & {84.2} & {51.9} & {41.8} & {61.7} & {92.3} & {94.1} & {77.8} & {93.7} & {83.4} & 90.8 & {78.6} & {86.9} & {91.5} \\

		& {ACMM} & {\bf 90.7} & {93.0} & {\bf 80.0} & {64.8} & {83.9} & {\bf 98.2} & {\bf 98.4} & {90.4} & {\bf 97.3} & {\bf 94.7} & {\bf 93.4} & {\bf 91.7} & {\bf 95.1} & {\bf 98.0} \\

		& {ACMP$\backslash$G} & 85.1 & 90.7 & 61.3 & 66.6 & 74.8 & 94.5 & 96.6 & 86.1 & 93.7 & 88.6 & 90.7 & 80.0 & 86.8 & 95.3 \\
		& {ACMP} & 90.6 & {\bf 95.4} & 72.4 & {\bf 78.0} & {\bf 84.2} & 96.8 & 98.2 & {\bf 92.4} & 96.1 & 94.4 & 93.2 & 87.5 & 91.5 & 97.9 \\
		\bottomrule
	\end{tabular}
	\label{tab:depthmap}
\end{table*}

\begin{figure*}
	\centering
	\includegraphics[width=0.119\textwidth]{fig4_R1_DSC_0299.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_ground_truth.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_COLMAP.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_openMVS.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_ACMH.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_ACMM.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_ACMP-G.png}
	\includegraphics[width=0.119\textwidth]{fig4_R1_ACMP.png} \\
	\includegraphics[width=0.119\textwidth]{fig4_R2_DSC_0220.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_ground_truth.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_COLMAP.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_openMVS.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_ACMH.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_ACMM.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_ACMP-G.png}
	\includegraphics[width=0.119\textwidth]{fig4_R2_ACMP.png} \\
	\includegraphics[width=0.119\textwidth]{fig4_R3_DSC_0267.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_ground_truth.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_COLMAP.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_openMVS.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_ACMH.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_ACMM.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_ACMP-G.png}
	\includegraphics[width=0.119\textwidth]{fig4_R3_ACMP.png} \\
	\begin{tabular}{>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}>{\centering}p{0.1\textwidth}}
		Image & GT & COLMAP & openMVS & ACMH & ACMM & ACMP$\backslash$G & ACMP
	\end{tabular}
	\caption{Qualitative depth map map comparisons of different methods on some high-resolution multi-view training datasets (courty., offi. and terrace) of ETH3D benchmark. Black pixels in GT mean no ground truth data.}
	\label{fig:dme}
\end{figure*}

\section{Experiments}

\noindent{\bf Datasets} We evaluate the effectiveness of our method on high-resolution multi-view stereo dataset of ETH3D benchmark~\cite{Schops2017Multi}. This dataset contains images at a resolution of $6048\times4032$ with calibration. Following \cite{Schonberger2016Pixelwise,Xu2018Multi}, we resize this imagery to no more than $3200$ pixels for each dimension while keeping the original aspect ratio. The dataset is further split into training datasets and test datasets. Besides ground truth point clouds, ground truth depth maps are also provided for training datasets. Thus, we first evaluate the depth estimation on training datasets. As for test datasets, we submit our reconstructed point clouds to the benchmark's website~\cite{Schops2017ETH3D} to evaluate them.

\noindent{\bf Evaluation Metrics} In depth map evaluation, we calculate the percentage of pixels with an absolute depth error less than $2cm$ and $10cm$ from ground truth. For point cloud evaluation, we assess reconstructed point clouds in terms of accuracy, completeness and $F_{1}$ score.

\noindent{\bf Parameter Settings} Our methods are implemented in C++ with CUDA and executed on a machine with two Intel E5-2630 CPUs and two GTX Titan X GPUs. $\{\epsilon,\alpha,\gamma, \lambda_\text{n}, \sigma,\eta,\lambda_\text{geo},\tau_\text{geo}\}=\{0.1,0.18,0.5,5^\circ,0.3,0.9,0.1,5.0\}$. Besides, $\lambda_\text{d}$ is adaptively set to one sixty-fourth of the depth interval of every reference image. We conduct geometric consistency twice as \cite{Xu2018Multi} does.

\noindent{\bf Depth Map Evaluation} We compare our method with some state-of-the-art PatchMatch multi-view stereo methods in depth map evaluation, including COLMAP~\cite{Schonberger2016Pixelwise}, openMVS~\cite{openMVS}, ACMH~\cite{Xu2018Multi} and ACMM~\cite{Xu2018Multi}. These methods are directly operated on the original resolution images except for ACMM with multi-scale scheme. We denote our method as ACMP because our planar prior assisted model is based on adaptive checkerboard sampling and propagation.

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.16\textwidth]{fig5_R1_DSC_1543.png}
	\includegraphics[width=0.16\textwidth]{fig5_R1_COLMAP.png}
	\includegraphics[width=0.16\textwidth]{fig5_R1_openMVS.png}
	\includegraphics[width=0.16\textwidth]{fig5_R1_ACMH.png}
	\includegraphics[width=0.16\textwidth]{fig5_R1_ACMM.png}
	\includegraphics[width=0.16\textwidth]{fig5_R1_ACMP.png}
	\includegraphics[width=0.16\textwidth]{fig5_R2_DSC_1140.png}
	\includegraphics[width=0.16\textwidth]{fig5_R2_COLMAP.png}
	\includegraphics[width=0.16\textwidth]{fig5_R2_openMVS.png}
	\includegraphics[width=0.16\textwidth]{fig5_R2_ACMH.png}
	\includegraphics[width=0.16\textwidth]{fig5_R2_ACMM.png}
	\includegraphics[width=0.16\textwidth]{fig5_R2_ACMP.png}
    \begin{tabular}{p{0.14\textwidth}<{\centering}p{0.14\textwidth}<{\centering}p{0.14\textwidth}<{\centering}p{0.14\textwidth}<{\centering}p{0.14\textwidth}<{\centering}p{0.14\textwidth}<{\centering}}
	Images & COLMAP & openMVS & ACMH & ACMM & ACMP
    \end{tabular}
	\caption{Qualitative point cloud comparisons of different MVS methods on living room and old computer of ETH3D benchmark. These 3D models are reported by the ETH3D benchmark evaluation server~\cite{Schops2017ETH3D}.}
	\label{fig:pce}
\end{figure*}

We list comparison results on $13$ high resolution multi-view training datasets of ETH3D benchmark in Table~\ref{tab:depthmap}. In order to validate the effectiveness of our novel multi-view aggregated matching cost, we remove the geometric consistency from our method and denote this method as ACMP$\backslash$G. We first compare ACMP$\backslash$G with COLMAP, openMVS and ACMH. As can be seen, ACMP$\backslash$G is much better than these methods. From the qualitative results in Figure~\ref{fig:dme}, we observe that ACMP$\backslash$G can estimate depth information of low-textured areas well. Moreover, ACMP$\backslash$G can also tackle the depth estimation in non-planar regions because our planar prior assisted multi-view aggregated matching cost simultaneously considers the photometric consistency.

As ACMP$\backslash$G does not consider the geometric consistency, its estimated depth maps contain some noise caused by inappropriate planar models. Therefore, ACMP combines ACMP$\backslash$G with geometric consistency to handle this problem. This makes our method competitive with ACMM. Note that, ACMM uses multi-scale geometric consistency to tackle the depth estimation in low-textured areas. However, due to its limited scales and lost image information at its coarsest scale, ACMM sometimes cannot obtain good estimation for low-textured areas at the coarsest scale. This can be reflected by the depth estimation of offi. dataset in Figure~\ref{fig:dme}. Differently, as ACMP adaptively captures the discrimination of different sizes according to triangular primitives, ACMP performs much better than ACMM in offi. dataset.

\begin{table}[!htb]
	\caption{Accuracy, completeness and $F_{1}$ score (in $\%$) comparisons of reconstructed point clouds on the high-resolution multi-view test datasets of ETH3D benchmark at evaluation threshold $2cm$. The related values are from \cite{Schops2017ETH3D}.}
	\centering
	\footnotesize
	\begin{tabular}{ccccc}
		\toprule
		& method & Accuracy & Completeness & $F_{1}$ \\
		\midrule
		\multirow{5}{*}{indoor} & COLMAP & {\bf 91.95} & 59.65 & 70.41 \\
		& openMVS & 82.00 & {\bf 75.92} & 78.33 \\
		& ACMH & 91.14 & 64.81 & 73.93 \\
		& ACMM & 90.99 & 72.73 & 79.84 \\
		& ACMP & 90.60 & 74.23 & {\bf 80.57} \\
		\midrule
		\multirow{5}{*}{outdoor} & COLMAP &  {\bf 92.04} & 72.98 & 80.81 \\
		& openMVS & 81.93 & {\bf 86.41} & 84.09 \\
		& ACMH & 83.96 & 80.03 & 81.77 \\
		& ACMM & 89.63 & 79.17 & 83.58 \\
		& ACMP & 90.35 & 79.62 & {\bf 84.36} \\
		\midrule
		\multirow{5}{*}{all} & COLMAP & {\bf 91.97} & 62.98 & 73.01 \\
		& openMVS & 81.98 & {\bf 78.54} & 79.77 \\
		& ACMH & 89.34 & 68.62 & 75.89 \\
		& ACMM & 90.65 & 74.34 & 80.78  \\
		& ACMP & 90.54 & 75.58 & {\bf 81.51} \\
		\bottomrule
	\end{tabular}
\label{tab:pointcloud}
\end{table}

\noindent{\bf Point Cloud Evaluation} The quantitative results of reconstructed point clouds are listed in Table~\ref{tab:pointcloud} and quantitative results are shown in Figure~\ref{fig:pce}. In the case of $2cm$, ACMP achieves the best $F_{1}$ score among all methods due to our better estimated depth maps. As for the completeness of 3D models, openMVS produces the best performance as it employs a different fusion scheme with a relaxed number of consistent views for a pixel, which leads to noisy point clouds with low accuracy and high completeness. As illustrated in Figure~\ref{fig:pce}, the 3D models of openMVS are not photo-realistic. Differently, our method can achieve a better trade-off between accuracy and completeness. This makes our estimated 3D models be applicable in the actual environment.

\begin{table}
	\caption{Running time of different stages of our method for an image of size $3200\times2130$ pixels on a single GPU.}
	\centering
	\footnotesize
	\begin{tabular}{ccc}
		\toprule
		Stage & Time (s) & Ratio ($\%$) \\
		\midrule
		Sparse Correspondences Generation & 6.40 & 30.9 \\
		Planar Model Construction & 1.08 & 5.2 \\
		Planar Prior Assistance & 5.43 & 26.2 \\
		Geometric Consistency & 7.79 & 37.6 \\
		\midrule
		Total Time & 20.7 & - \\
		\bottomrule
	\end{tabular}
\label{tab:Time}
\end{table}

\noindent{\bf Runtime Analysis} We run our method on a single GPU and record the running time of each stage in Table~\ref{tab:Time}. As can be seen, the planar model construction occupies very little runtime. As for sparse correspondences generation, planar prior assistance and geometric consistency, their running time is very close. This is because these stages all employ the same pipeline of PatchMatch multi-view stereo. Therefore, with very little computational cost, our method without geometric consistency can achieve much better reconstruction results than other PatchMatch multi-view stereo methods that are implemented on the original image resolution.

\section{Conclusion}

In this work, we propose a planar prior assisted PatchMatch multi-view stereo framework to help the depth estimation in low-textured areas. To tackle depth ambiguities caused by unreliable photometric consistency, we leverage sparse credible correspondences to build planar models. These models can reflect the depth ranges of low-textured areas well but are still biased, especially for non-planar regions. Therefore, we embed these planar models into PatchMatch multi-view stereo by utilizing a probabilistic graphical model. This derives a novel multi-view aggregated matching cost, which jointly consists of photometric consistency and planar priors. This makes our method suited for both planar and non-planar regions. Experiments on ETH3D benchmark demonstrate the effectiveness of our methods by yielding state-of-the-art performance. We note that the performance of our method is comparable to ACMM. Since ACMM combines ACMH with the multi-scale geometric consistency, we think its performance gain mainly comes from the multi-scale geometric consistency framework. As a separate module, we have demonstrated that our method is much better than ACMH in our experiments. Thus, we may combine our method with the multi-scale geometric consistency to improve the reconstruction performance in the future work.

\noindent\textbf{Acknowledgements} This work was supported by the National Natural Science Foundation of China under Grants 61772213 and 91748204.

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


text:
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Notable Athletic Alumn 1930's
Harry Adelman graduated in 1940. He was an All LA City second team football selection in 1939. He lettered in football at USC as a guard and end in 1941-42.
Manuel Alba graduated in 1943 and placed second in the 440 at the LA City Track Finals in 1943. He later lettered in track at Pepperdine.
Art Aragon attended Roosevelt in the early forties and went on to become one of the most popular boxers in Los Angeles history. Known as "The Golden Boy", Aragon was the top rated lightweight in the World in 1951. He fought Jimmy Carter for the title, but was defeated. In 109 bouts, he lost only 16 and won 61 by knockout.
Pete Berokoff graduated in 1941 and finished second in the LA City Track Finals pole vault. He later attended Occidental where he was the 1949/SCIAC pole vault champion.
Tony Brizula graduated in 1949. He was the LA City Gymnastics rings champion in 1948 and 1949. He placed third in the National AAU Gymnastics Championships in 1949.
Gil Cadilli graduated in 1949. From 1950 thru 1959 he was a well regarded featherweight in the professional boxing ranks.
Al Cantor graduated in 1943. He lettered in football at USC as a halfback in 1948.
Marty Grbovaz graduated in 1942 and was the All LA City Football Player of the Year in 1941. An outstanding kicker, he was a starting end at USF in 1946.
Bill Harmatz graduated in 1949 and was the LA City parallel bar champion that year. Following high school, he became a jockey. The highlight of his career was a victory in the Preakness in 1959 aboard Royal Orbit.
Arnie Klein graduated in 1947. While at Roosevelt, he finished third in the All LA City Track Finals high jump in 1946 and sixth in the shot put in 1947. He was even stronger in gymnastics. He won the long horse in the 1946 LA City Gymnastics Finals and he won the rings in 1945 thru 1947. In 1950 he finished third in the National AAU rings.
John Leon graduated in 1943 and was an All LA City Football pick in 1942. He lettered in football at Pepperdine in 1948 and 1949. He later became a ranking administrator in the LA City School District. His son. Rich, played football at USC.
Harry Malloy attended Roosevelt and finished fifth in the National AAU Gymnastics Indian Clubs in 1948.
Marvin Milkes graduated in 1942. He began his career in baseball as a batboy for the Los Angeles Angels of the Pacific Coast League. He served as Sports Editor at Roosevelt and at LACC. He was selected as Minor League Baseball Executive of
the Year in 1956. He served as Assistant General. Manager for the expansion Los Angeles Angels. He became General Manager of the expansion Seattle Pilots in 1969.
John Mitzushima graduated in the late.1940's and became the 1952 Pacific Coast Conference rings champion in 1952, while attending UCLA.
Sam Mizrahi graduated in 1947 and was second in the long horse in the LA City Gymnastics Finals. He finished sixth in the National AAU long horse in 1949 while attending East LA JC.
Mary Novikoff, sister of famous Lou Novikoff, graduated in 1940. She played in the semi-professional National Girls Softball League in the early 1940's.
Tom Novikoff graduated in the late 40^s. He lettered in football as a fullback at Oregon in 1951 and 1952. During the 1952 season, he gained 486 yards on 123 carries.
Al Padilla graduated in 1946 and went on to Occidental, where he was selected to the All Conference team in 1951.
Henry Porras graduated in 1947 and was the LA City rope climb champ that year. He also finished second in the National AAU rope climb that year.
Manuel Ronquillo graduated in 1949 and finished second in the LA City Track Finals high jump that year. He also finished second in the state championships that season. He served as Student Body President in his senior year. He attended
USC, where he placed fourth in the 1952 NCAA high jump.
Dick Shivers graduated from Roosevelt and went on to Occidental where he finished fifth in the National AAU pole vault in 1952.
Sanford Werner placed-.third in the LA City Gymnastics rope climb, the year he graduated in 1949. He was a medallist six times in the rope climb at the National AAU meet, equalling the world record for 20 feet, while winning that championship in 1951, while attending LA City College, later competing at Cal State LA, and still competing in the '90s.
George Wickler graduated in 1948 and placed second in the LA City Gymnastics rings that year. He was the National AAU rings champion in 1950, 1952, and 1953. He also was the NCAA rings champion in 1955, while attending USC.


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Amul, a brand of dairy cooperative major Gujarat Cooperative Milk Marketing Federation Ltd (GCMMF), is ranked 8th in the list of top 20 global dairy processors released by the International Farm Comparison Network (IFCN). Amul was ranked at 18th position in 2012, however, in flat 8 years, it has moved 10 notches above to the current 8th position.
Aviation industry to lose Rs 21,000 crore in FY21: ICRA
ICRA Research projected that the Indian aviation industry would suffer losses worth Rs 21,000 crore in the current fiscal. The ratings agency, in its latest report on the Indian aviation industry's capacity and passenger growth, maintained a negative credit outlook on the Indian aviation industry, owing to the coronavirus crisis.


text:
Q: UnhandledPromiseRejectionWarning: Error: Cannot find module 'svelte/compiler' I am trying Svelte 3 but it's throwing an error when running. I have tried two simple configurations mentioned below but they don't work.

*

*Module svelte is installed both locally and globally.

*Module svelte is installed just locally

$npm run dev

(node:41186) UnhandledPromiseRejectionWarning: Error: Cannot find module 'svelte/compiler'
        at Function.Module._resolveFilename (internal/modules/cjs/loader.js:594:15)
        at Function.Module._load (internal/modules/cjs/loader.js:520:25)
        at Module.require (internal/modules/cjs/loader.js:650:17)
        at require (internal/modules/cjs/helpers.js:20:18)
        at Object.<anonymous> (/Users/skc/.npm/_npx/41186/lib/node_modules/sapper/dist/core.js:16:30)
        at Module._compile (internal/modules/cjs/loader.js:702:30)
        at Object.Module._extensions..js (internal/modules/cjs/loader.js:713:10)
        at Module.load (internal/modules/cjs/loader.js:612:32)
        at tryModuleLoad (internal/modules/cjs/loader.js:551:12)
        at Function.Module._load (internal/modules/cjs/loader.js:543:3)
    (node:41186) UnhandledPromiseRejectionWarning: Unhandled promise rejection. This error originated either by throwing inside of an async function without a catch block, or by rejecting a promise which was not handled with .catch(). (rejection id: 1)
    (node:41186) [DEP0018] DeprecationWarning: Unhandled promise rejections are deprecated. In the future, promise rejections that are not handled will terminate the Node.js process with a non-zero exit code.
    created dist/module.js, dist/index.js in 5.5s

A: I had a similar issue and fixed it by using Node.js v14.5.0 where's before I was using v.13.2.0.
The warning I got: (node:22755) UnhandledPromiseRejectionWarning: Error: No valid exports main found for '/home/user/dev/coding-tests/svelte/realworld/node_modules/@rollup/pluginutils'
This happened when running npm run dev


Here are links to game with source code and also documentation about the game formats. Some of them are commercial games that have released source code and other are ones that was open source. Links to more games can be found at http://osgameclones.com.


text:
Q: What is the most efficient way of finding and replacing multiple strings of a cell in a range? **Background **

*

*I am working on a code for making combos in a fighting game

*I need to take a range, then convert certain numbers and/or letters  into other Numbers/and/or letters with additional characters/spacing so the program registers the combo.
Right now I have some code that gets the job done but I don't believe this is the "right" nor efficient way to do this since In all there will be about 40 "switches" to be set up:
 function printCombo()  {
//general set up
  const ss=SpreadsheetApp.getActive();
  const psh=ss.getSheetByName('Print Sheet');
  const scanrange= psh.getRange("Range to be scanned");

//set constants and text they are being replaced with
  const 1A = scanrange.createTextFinder('1A');
  1A.replaceAllWith('1 A');
  const 2B = scanrange.createTextFinder('2B');
  2B..replaceAllWith('2   B');
  const 3C = scanrange.createTextFinder('3C');
  3C.replaceAllWith('');
  const 4D = scanrange.createTextFinder('4D');
  4D.replaceAllWith('4 ]D[ 4');
  const xy = scanrange.createTextFinder('xy');
  4D.replaceAllWith('xy's replacement');
}

Also, if there are any beginner guides around this sort of subject, please let me know, as I'm still a touch and go beginner when it comes to the logic of script coding and order of operations
Thanks!!

Bonus Question:
Is there a way to make it so that if a cell is "Bold"/ some other form of styling, it places a ][ around whatever character of that cell is bolded? for example
"3B" would become "3 ]B[" instead of "3 B" <= this would be the end game of this replacement process, and make me have to list less switches, but if that's too complicated then no worries.

A: I believe your goal as follows.

*

*You want to reduce the process cost of your script.

*In your situation, you want to replace the texts with about 40 patterns.

Modification points:

*

*I think that the variables of const 1A =, const 2B = and so on cannot be used. Please be careful this.

*About 4D.replaceAllWith('xy's replacement');, in this case, xy's replacement is not enclosed. Please be careful this.

*About 2B..replaceAllWith('2   B');, .. is not correct.

*In order to reduce the process cost of your script, in this answer, I would like to propose to use Sheets API. When the Sheets API is used, replacing the texts with about 40 patterns can be done by one API call. By this, I thought that the process cost will be able to be reduced.

When above points are reflected to your script, it becomes as follows.
Modified script:
Before you use this script, please enable Sheets API at Advanced Google services.
function printCombo() {
  //general set up
  const ss = SpreadsheetApp.getActive();
  const psh=ss.getSheetByName('Print Sheet');
  const scanrange= psh.getRange("Range to be scanned");

  // I modified below script.
  // Please set the replace patterns for this object. This object uses your patterns.
  const replacePatterns = [
    { search: '1A', replace: '1 A' },
    { search: '2B', replace: '2   B' },
    { search: '3C', replace: '' },
    { search: '4D', replace: '4 ]D[ 4' },
    { search: 'xy', replace: "xy's replacement" }
  ];
  const sheetId = psh.getSheetId();
  const startRowIndex = scanrange.getRow() - 1;
  const endRowIndex = startRowIndex + scanrange.getNumRows();
  const startColumnIndex = scanrange.getColumn() - 1;
  const endColumnIndex = startColumnIndex + scanrange.getNumColumns();
  const requests = replacePatterns.map(({ search, replace }) => ({ findReplace: { range: { sheetId: sheetId, startRowIndex: startRowIndex, endRowIndex: endRowIndex, startColumnIndex: startColumnIndex, endColumnIndex: endColumnIndex }, find: search, replacement: replace } }));
  Sheets.Spreadsheets.batchUpdate({ requests: requests }, ss.getId());
}

*

*When above script is run, the cell values of scanrange are replaced using the replace patterns of replacePatterns.

Note:

*

*About your Bonus Question, in that case, I think that the other process from above modification might be required to be used. So in this case, I would like to recommend to post it as new question.

References:

*

*Method: spreadsheets.batchUpdate of Sheets API

*FindReplaceRequest
Added:
From your following replying,

this is an awesome solve, however with this being a script that I would need to be able for people to quickly understand and use, is there any way to do this sort of setup without activating the advanced sheets API situation?

How about the following sample script?
function printCombo() {
  //general set up
  const ss = SpreadsheetApp.getActive();
  const psh=ss.getSheetByName('Print Sheet');
  const scanrange= psh.getRange("Range to be scanned");

  // I modified below script.
  // Please set the replace patterns for this object. This object uses your patterns.
  const replacePatterns = [
    { search: '1A', replace: '1 A' },
    { search: '2B', replace: '2   B' },
    { search: '3C', replace: '' },
    { search: '4D', replace: '4 ]D[ 4' },
    { search: 'xy', replace: "xy's replacement" }
  ];
  replacePatterns.forEach(({ search, replace }) => scanrange.createTextFinder(search).replaceAllWith(replace));
}


text:
Le musée d'État d'histoire de l'Oural du Sud (Государственный исторический музей Южного Урала) est le principal musée d'histoire de l'oblast de Tcheliabinsk en Russie. Il se trouve à Tcheliabinsk et a été ouvert en 1923. Jusqu'en 2016, il s'appelait le musée d'histoire régionale de Tcheliabinsk. Il est dirigé depuis 2004 par Vladimir Bogdanovski.

Histoire

En 1913, un groupe d'enthousiastes mené par Ippolit Kracheninnikov commence à réunir une collection pour le musée. Après une interruption due à la Révolution d'Octobre et à la guerre civile qui s'ensuit, une décision des autorités locales réquisitionne à l'automne 1922 l'ancienne demeure des frères Iouchev, anciens marchands fortunés de la ville, afin d'y installer les collections, rue du Travail. Le musée est inauguré le 1er juillet 1923. De 1929 à 1933, le musée occupe différents bâtiments. De mai 1933 à 1989, il est installé dans l'ancienne église de la Sainte-Trinité, puis en 1989, il s'installe au rez-de-chaussée d'un immeuble de la perspective Lénine, tandis que le fond de la collection est déposé dans un bâtiment rue Kaslinskaïa.
Pendant la guerre, jusqu'à l'été 1944, il est fermé.

Le 29 juin 2006, le musée s'installe dans un édifice nouvellement construit avec toutes les capacités modernes et des installations multimédia.

Le musée expose le plus grand des fragments trouvés tombé en 2013 de la météorite de Tcheliabinsk, pesant une demi-tonne.
En 2016, le musée prend son appellation actuelle.

Données actuelles
Le fond comprend plus de 250 mille objets.

Expositions permanentes:
 Salle d'histoire naturelle et d'histoire ancienne
 Salle d'histoire folklorique et de la vie populaire
 Salle d'histoire du XXe siècle
Le musée possède des photographies anciennes, des tableaux du XVIIIe siècle au XXe siècle, d'anciens documents et des livres rares, des pièces et vêtements folkloriques de la vie populaire de paysans russes, de tribus tatares et bachkires et de tribus nomades, des moulages d'art, des gravures de Zlatooust, des pièces de monnaie anciennes, des collections minéralogiques, des ossements d'animaux disparus et des animaux empaillés.

Les découvertes archéologiques stockées ici, trouvées dans le sud de l'Oural, datent de l'énéolithique au début du Moyen Âge.

Notes et références

Voir aussi
Superbolide de Tcheliabinsk

Liens externes
 Viste du musée en 3D

Tcheliabinsk
Tcheliabinsk
Tcheliabinsk

redpajama_set_name: RedPajamaWikipedia