Summary of Unsupervised Occupancy Learning From Sparse Point Cloud, by Amine Ouasfi and Adnane Boukhayma
Unsupervised Occupancy Learning from Sparse Point Cloud
by Amine Ouasfi, Adnane Boukhayma
First submitted to arxiv on: 3 Apr 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 A novel approach to inferring 3D shapes from point clouds is presented in this paper. The authors propose learning occupancy fields instead of Neural Signed Distance Functions (SDFs), which are more challenging to learn from sparse inputs. To do so, they employ a margin-based uncertainty measure to sample points along the decision boundary of the occupancy function and supervise them using the input point cloud. Additionally, they introduce a bias towards minimal entropy fields during early training stages. The authors demonstrate the efficacy of their method through extensive experiments and evaluations on both synthetic and real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to understand 3D shapes from point clouds. It’s hard to learn this information when there isn’t any truth to compare it to. To make it easier, the authors suggest learning something called occupancy fields instead of Neural Signed Distance Functions (SDFs). They use a special tool to pick points that are close to being correct and help them get better. This makes it easier to figure out what the shape is like. The paper shows this works well on fake and real data. |
