Tensor-Train Point Cloud Compression and Efficient Approximate Nearest Neighbor Search
Primary Area: general machine learning (i.e., none of the above)
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Keywords: Nearest neighbor search, Approximate Search, Information Storage and Retrieval
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Abstract: Nearest-neighbor search in large vector databases is crucial for various machine learning applications.
This paper introduces a novel method using **tensor-train** (TT) low-rank tensor decomposition to efficiently represent point clouds and enable fast approximate nearest-neighbor searches.
We propose a probabilistic interpretation and utilize density estimation losses like Sliced Wasserstein to train TT decompositions, resulting in robust point cloud compression.
We reveals an inherent hierarchical structure within TT point clouds, facilitating efficient approximate nearest-neighbor searches.
In our paper, we provide detailed insights into the methodology and conduct comprehensive comparisons with existing methods.
We demonstrate its effectiveness in various scenarios, including out-of-distribution (OOD) problems and approximate nearest-neighbor (ANN) search tasks.
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Submission Number: 9087
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