Summary of Neural Varifolds: An Aggregate Representation For Quantifying the Geometry Of Point Clouds, by Juheon Lee et al.
Neural varifolds: an aggregate representation for quantifying the geometry of point clouds
by Juheon Lee, Xiaohao Cai, Carola-Bibian Schönlieb, Simon Masnou
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 surface geometry characterisation approach is introduced in this paper, combining deep learning techniques with geometric fidelity metrics. The authors propose a neural varifold representation of point clouds, which captures not only the surface geometry but also subtle geometric consistencies on the surface. Neural network-based algorithms are developed to compute the varifold norm between two point clouds, and evaluated on three tasks: shape matching, few-shot shape classification, and shape reconstruction. The results demonstrate that the proposed neural varifold outperforms state-of-the-art methods in shape matching and few-shot shape classification, and is competitive for shape reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point clouds are a way to represent 3D objects using many points with their locations and connections. This makes it easy to understand how things look from different sides. Some researchers have been trying to find better ways to describe these point clouds. They’ve been using special kinds of math problems, like optimal transportation costs. In this paper, they come up with a new way to describe the surface of these point clouds. It’s called a neural varifold representation. This method is good at telling us about both the shape and the tiny details on the surface. The authors also develop algorithms to use this method for three important tasks: matching shapes, recognizing shapes after seeing them only briefly, and reconstructing shapes from incomplete information. |
Keywords
» Artificial intelligence » Classification » Deep learning » Few shot » Neural network
