Summary of Statistical Edge Detection and Udf Learning For Shape Representation, by Virgile Foy (imt) et al.
Statistical Edge Detection And UDF Learning For Shape Representation
by Virgile Foy, Fabrice Gamboa, Reda Chhaibi
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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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 The proposed Neural Distance Function (NDF) learns Unsigned Distance Functions (UDFs) that improve the fidelity of 3D surface representation for tasks like representation learning, surface classification, and reconstruction. A key challenge is to concentrate the learning effort on surface edges, which allows for better local accuracy and global expressiveness. To achieve this, a statistical method based on p-value calculation detects surface edges more accurately than a local geometric descriptor. The NDF is trained with more points around surface edges, demonstrating improved Hausdorff distance-based expressiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize the shape of an object from different angles. One way to do this is by using a special kind of map called an unsigned distance function (UDF). This UDF helps the computer understand the shape of the object, but it can be tricky to create one that’s accurate and useful. Researchers have developed a new method to make these UDFs better by focusing on areas where the surface of the object is changing rapidly. This makes the map more detailed and helpful for tasks like recognizing objects or reconstructing 3D shapes. |
Keywords
» Artificial intelligence » Classification » Representation learning
