Answers.Body: <blockquote>
  <p>&quot;... I am looking for reference on computer vision algorithms that does not require learning or training on datasets. Rather, topics arising from &quot;structure from motion&quot; ...&quot;.</p>
  </blockquote>
  <p>Here is a list of papers, blogs, and code that you might find helpful:</p>
  <ul>
  <li><p>The website PapersWithCode's search engine returns several hits for &quot;<a href="https://paperswithcode.com/search?q_meta=&amp;q_type=&amp;q=NRSfM" rel="nofollow noreferrer">NRSfM</a>&quot;, a few of the projects specifically mention limited datasets:</p>
  <ol>
  <li><p>&quot;<a href="https://paperswithcode.com/paper/deep-interpretable-non-rigid-structure-from" rel="nofollow noreferrer">Deep Interpretable Non-Rigid Structure from Motion</a>&quot;, (Feb 28 2019), by Chen Kong and Simon Lucey. <a href="https://github.com/kongchen1992/deep-nrsfm" rel="nofollow noreferrer">GitHub</a>.</p>
  <blockquote>
  <p>&quot;Once the network's weights are estimated (for a non-rigid object) we show how our approach can effectively recover 3D shape from a single image -- outperforming comparable methods that rely on direct 3D supervision.&quot;.</p>
  </blockquote>
  </li>
  <li><p>&quot;<a href="https://paperswithcode.com/paper/local-non-rigid-structure-from-motion-from" rel="nofollow noreferrer">Local Non-Rigid Structure-From-Motion From Diffeomorphic Mappings</a>&quot;,
  (CVPR 2020), by Shaifali Parashar, Mathieu Salzmann, and Pascal Fua. <a href="https://github.com/cvlab-epfl/diff-nrsfm" rel="nofollow noreferrer">GitHub</a>.</p>
  <blockquote>
  <p>&quot;We show that it is nevertheless sufficient to establish local correspondences between the surface in two different images and therefore to perform point-wise reconstruction using only first-order derivatives. &quot;.</p>
  </blockquote>
  </li>
  <li><p>&quot;<a href="https://paperswithcode.com/paper/mhr-net-multiple-hypothesis-reconstruction-of" rel="nofollow noreferrer">MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views</a>&quot;, (July 19, 2022), by Haitian Zeng, Xin Yu, Jiaxu Miao, and Yi Yang. (No code implementations yet).</p>
  <blockquote>
  <p>&quot;We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net.&quot;.</p>
  </blockquote>
  </li>
  </ol>
  </li>
  <li><p>arXiv's search engine returns 31 hits for &quot;<a href="https://arxiv.org/search/?query=NRSfM&amp;searchtype=all&amp;source=header" rel="nofollow noreferrer">NRSfM</a>&quot;, a few of the papers are:</p>
  <ul>
  <li><p>The arXiv paper &quot;<a href="https://arxiv.org/abs/2207.06262" rel="nofollow noreferrer">Organic Priors in Non-Rigid Structure from Motion</a>&quot;, (July 16, 2022), by Suryansh Kumar and Luc Van Gool, advocates the use of organic priors in classical non-rigid structure from motion (NRSfM). They outline how to effectively recover motion and non-rigid 3D shape using the proposed organic prior based approach and demonstrate results that outperform prior-free NRSfM performance by a significant margin.</p>
  <p>A quote from Suryansh Kumar's blog &quot;<a href="https://suryanshkumar.github.io/Non-Rigid-Structure-from-Motion/" rel="nofollow noreferrer">Non-Rigid Structure from Motion</a>&quot;:</p>
  <blockquote>
  <p>&quot;To date, no algorithm can solve NRSfM for all kinds of conceivable motion. Consequently, additional constraints, priors, and assumptions are often employed to solve NRSfM.&quot;.</p>
  </blockquote>
  <p>His <a href="https://github.com/suryanshkumar?tab=repositories" rel="nofollow noreferrer">GitHub</a> contains some NRSfM code.</p>
  </li>
  <li><p>&quot;<a href="https://arxiv.org/abs/2005.04810" rel="nofollow noreferrer">A Closed-Form Uncertainty Propagation in Non-Rigid Structure from Motion</a>&quot;, (May 10, 2020), by Jingwei Song, Mitesh Patel, Ashkan Jasour, and Maani Ghaffari. YouTube <a href="https://youtu.be/UFS8txdn68U" rel="nofollow noreferrer">Video</a>, <a href="https://github.com/JingweiSong/NRSfM_uncertainty" rel="nofollow noreferrer">GitHub</a>.</p>
  </li>
  <li><p>&quot;<a href="https://arxiv.org/abs/2001.10090" rel="nofollow noreferrer">Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild</a>&quot;, (Jan 27, 2020), by Chaoyang Wang, Chen-Hsuan Lin, Simon Lucey. GitHub <a href="https://github.com/MightyChaos/deepNRSfMpp" rel="nofollow noreferrer">without code</a>, GitHub <a href="https://github.com/kongchen1992/deep-nrsfm" rel="nofollow noreferrer">with code</a>.</p>
  <blockquote>
  <p>&quot;Deep NRSfM++, achieves state-of-the-art performance across numerous large-scale benchmarks, outperforming both classical and learning-based 2D-3D lifting methods.&quot;.</p>
  </blockquote>
  </li>
  </ul>
  </li>
  </ul>
  <p>Some other papers are:</p>
  <ul>
  <li><p>In the arXiv paper: &quot;<a href="https://arxiv.org/abs/1708.04672" rel="nofollow noreferrer">DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image</a>&quot; (Aug 11, 2017), by Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher Choy, Silvio Savarese, they promise to make <a href="https://deformnet-site.github.io/DeformNet-website/" rel="nofollow noreferrer">code available</a> later.</p>
  </li>
  <li><p>The website x-mol's search engine returns 65 hits for &quot;<a href="https://en.x-mol.com/paper/search?option=NRSfM" rel="nofollow noreferrer">NRSfM</a>&quot;. That site doesn't clearly present which papers have code available and which do not, so there's a lot to dig through.</p>
  <p>Adding the word &quot;code&quot; to the search balloons the number of search results. On the arXiv website adding the extra word greatly reduces the number of <a href="https://arxiv.org/search/?query=NRSfM+code&amp;searchtype=all&amp;abstracts=show&amp;order=-announced_date_first&amp;size=50" rel="nofollow noreferrer">returned results</a>; that gives more focus but may also miss some helpful papers.</p>
  <p>For example the paper &quot;<a href="https://en.x-mol.com/paper/detail/1344029044490903552" rel="nofollow noreferrer">A Benchmark and Evaluation of Non-Rigid Structure from Motion</a>&quot;, (Dec 29, 2020), by Sebastian Hoppe Nesgaard Jensen, Mads Emil Brix Doest, Henrik Aanæs &amp; Alessio Del Bue, only offers psuedo-code.</p>
  </li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-10-04T11:37:31.217Z
Answers.LastActivityDate: 2022-10-04T11:37:31.217Z
Answers.UserDisplayName: Rob
Body: <p>I am working on a project in which I will be performing object detection on deformed objects. Unfortunately, there isn't enough data sets to train them on some neural network. I am looking for reference on computer vision algorithms that does not require learning or training on datasets. Rather, topics arising from &quot;structure from motion&quot; that are popular within the computer vision community.</p>
  <p>Some note: The object detection will take place in an open environment rather close environment.</p>
  
Tags: <computer-vision><object-detection><unsupervised-learning>
Title: Survey on non-machine learning object detection algorithms
UserDisplayName: UserX
ThreadText: # Survey on non-machine learning object detection algorithms
  
  (Asked by: username_0 on 2022-09-26)
  
  I am working on a project in which I will be performing object detection on deformed objects. Unfortunately, there isn't enough data sets to train them on some neural network. I am looking for reference on computer vision algorithms that does not require learning or training on datasets. Rather, topics arising from "structure from motion" that are popular within the computer vision community.
  
  Some note: The object detection will take place in an open environment rather close environment.
  
  **Tags:** computer-vision, object-detection, unsupervised-learning
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-10-04. Score: 0 | Accepted answer
  
  > "... I am looking for reference on computer vision algorithms that does not require learning or training on datasets. Rather, topics arising from "structure from motion" ...".
  
  Here is a list of papers, blogs, and code that you might find helpful:
  
  * The website PapersWithCode's search engine returns several hits for "[NRSfM](https://paperswithcode.com/search?q_meta=&q_type=&q=NRSfM)", a few of the projects specifically mention limited datasets:
  
    1. "[Deep Interpretable Non-Rigid Structure from Motion](https://paperswithcode.com/paper/deep-interpretable-non-rigid-structure-from)", (Feb 28 2019), by Chen Kong and Simon Lucey. [GitHub](https://github.com/kongchen1992/deep-nrsfm).
  
       > "Once the network's weights are estimated (for a non-rigid object) we show how our approach can effectively recover 3D shape from a single image -- outperforming comparable methods that rely on direct 3D supervision.".
    2. "[Local Non-Rigid Structure-From-Motion From Diffeomorphic Mappings](https://paperswithcode.com/paper/local-non-rigid-structure-from-motion-from)",
       (CVPR 2020), by Shaifali Parashar, Mathieu Salzmann, and Pascal Fua. [GitHub](https://github.com/cvlab-epfl/diff-nrsfm).
  
       > "We show that it is nevertheless sufficient to establish local correspondences between the surface in two different images and therefore to perform point-wise reconstruction using only first-order derivatives. ".
    3. "[MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views](https://paperswithcode.com/paper/mhr-net-multiple-hypothesis-reconstruction-of)", (July 19, 2022), by Haitian Zeng, Xin Yu, Jiaxu Miao, and Yi Yang. (No code implementations yet).
  
       > "We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net.".
  * arXiv's search engine returns 31 hits for "[NRSfM](https://arxiv.org/search/?query=NRSfM&searchtype=all&source=header)", a few of the papers are:
  
    + The arXiv paper "[Organic Priors in Non-Rigid Structure from Motion](https://arxiv.org/abs/2207.06262)", (July 16, 2022), by Suryansh Kumar and Luc Van Gool, advocates the use of organic priors in classical non-rigid structure from motion (NRSfM). They outline how to effectively recover motion and non-rigid 3D shape using the proposed organic prior based approach and demonstrate results that outperform prior-free NRSfM performance by a significant margin.
  
      A quote from Suryansh Kumar's blog "[Non-Rigid Structure from Motion](https://suryanshkumar.github.io/Non-Rigid-Structure-from-Motion/)":
  
      > "To date, no algorithm can solve NRSfM for all kinds of conceivable motion. Consequently, additional constraints, priors, and assumptions are often employed to solve NRSfM.".
  
      His [GitHub](https://github.com/suryanshkumar?tab=repositories) contains some NRSfM code.
    + "[A Closed-Form Uncertainty Propagation in Non-Rigid Structure from Motion](https://arxiv.org/abs/2005.04810)", (May 10, 2020), by Jingwei Song, Mitesh Patel, Ashkan Jasour, and Maani Ghaffari. YouTube [Video](https://youtu.be/UFS8txdn68U), [GitHub](https://github.com/JingweiSong/NRSfM_uncertainty).
    + "[Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild](https://arxiv.org/abs/2001.10090)", (Jan 27, 2020), by Chaoyang Wang, Chen-Hsuan Lin, Simon Lucey. GitHub [without code](https://github.com/MightyChaos/deepNRSfMpp), GitHub [with code](https://github.com/kongchen1992/deep-nrsfm).
  
      > "Deep NRSfM++, achieves state-of-the-art performance across numerous large-scale benchmarks, outperforming both classical and learning-based 2D-3D lifting methods.".
  
  Some other papers are:
  
  * In the arXiv paper: "[DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image](https://arxiv.org/abs/1708.04672)" (Aug 11, 2017), by Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher Choy, Silvio Savarese, they promise to make [code available](https://deformnet-site.github.io/DeformNet-website/) later.
  * The website x-mol's search engine returns 65 hits for "[NRSfM](https://en.x-mol.com/paper/search?option=NRSfM)". That site doesn't clearly present which papers have code available and which do not, so there's a lot to dig through.
  
    Adding the word "code" to the search balloons the number of search results. On the arXiv website adding the extra word greatly reduces the number of [returned results](https://arxiv.org/search/?query=NRSfM+code&searchtype=all&abstracts=show&order=-announced_date_first&size=50); that gives more focus but may also miss some helpful papers.
  
    For example the paper "[A Benchmark and Evaluation of Non-Rigid Structure from Motion](https://en.x-mol.com/paper/detail/1344029044490903552)", (Dec 29, 2020), by Sebastian Hoppe Nesgaard Jensen, Mads Emil Brix Doest, Henrik Aanæs & Alessio Del Bue, only offers psuedo-code.
  
  
  ---
  
  