Summary of Few-shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries, by Amine Ouasfi et al.
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
by Amine Ouasfi, Adnane Boukhayma
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Implicit Neural Representations have gained popularity for capturing complex modalities. Within 3D shape representation, Neural Signed Distance Functions (SDF) have shown promise in encoding intricate geometry. However, learning SDFs from sparse point clouds without ground truth supervision remains challenging. Our method introduces a regularization term using adversarial samples around the shape to improve learned SDFs. Through extensive experiments and evaluations, our proposed method demonstrates its capacity to improve SDF learning compared to baselines and state-of-the-art methods using synthetic and real data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at recognizing shapes in 3D space. It uses a special way of representing shapes called Neural Signed Distance Functions (SDF). The problem is that it’s hard to teach computers to make these SDFs when all they have are some random points in space and no guidance on what the shape should look like. Our new method helps computers learn these SDFs by introducing fake “noise” around the real shape, which helps them get better at recognizing the shape. |
Keywords
» Artificial intelligence » Regularization
