Summary of On Optimal Sampling For Learning Sdf Using Mlps Equipped with Positional Encoding, by Guying Lin et al.
On Optimal Sampling for Learning SDF Using MLPs Equipped with Positional Encoding
by Guying Lin, Lei Yang, Yuan Liu, Congyi Zhang, Junhui Hou, Xiaogang Jin, Taku Komura, John Keyser, Wenping Wang
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper investigates neural implicit fields, specifically the neural signed distance field (SDF) of a shape, which have numerous applications in areas such as 3D shape encoding and collision detection. While Multi-layer Perceptrons (MLP) with positional encoding (PE) can effectively capture high-frequency geometric details, they often produce noisy artifacts in the learned implicit fields. The authors aim to explain this issue through Fourier analysis and propose a method to estimate the intrinsic frequency of the network based on randomized weights. By sampling against this intrinsic frequency, the recommended training sampling rate is determined, leading to accurate fitting results. The proposed strategy outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how neural networks can learn shapes in 3D space. Right now, these networks are great at capturing small details about shapes, but they sometimes make mistakes and show weird artifacts. The researchers wanted to figure out why this happens and found that it’s because the networks are trying to capture too many tiny details. They came up with a new way to teach the networks that lets them learn accurate shape representations without these mistakes. This is an important step forward in using neural networks for tasks like computer vision and robotics. |
Keywords
* Artificial intelligence * Positional encoding
