Summary of Tensor-train Point Cloud Compression and Efficient Approximate Nearest-neighbor Search, by Georgii Novikov et al.
Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search
by Georgii Novikov, Alexander Gneushev, Alexey Kadeishvili, Ivan Oseledets
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 introduces a novel method for efficient representation of point clouds using tensor-train (TT) low-rank tensor decomposition, enabling fast approximate nearest-neighbor searches. The proposed method uses probabilistic interpretation and density estimation losses like Sliced Wasserstein to train TT decompositions, resulting in robust point cloud compression. The hierarchical structure within TT point clouds facilitates efficient approximate nearest-neighbor searches. Comprehensive comparisons with existing methods demonstrate its effectiveness in various scenarios, including out-of-distribution (OOD) detection problems and approximate nearest-neighbor (ANN) search tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines quickly find the closest points to a given point when dealing with large amounts of data. The authors use a new way to represent these point clouds using tensor-train decomposition, which allows for fast searching. They also show that this method can be trained using special losses to make it more robust and efficient. This can be useful in many applications, such as detecting unusual points or finding similar points. |
Keywords
» Artificial intelligence » Density estimation » Nearest neighbor
