Novikov, G., Gneushev, A., Kadeishvili, A., & Oseledets, I. (2024). Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search. Proceedings of the International Conference on Optimization and Machine Learning (ICOMP 2024).
This paper proposes a new method for efficiently representing and searching large point clouds using tensor-train (TT) decomposition, aiming to address the limitations of traditional methods in terms of memory consumption and search speed.
The authors propose a probabilistic interpretation of point cloud compression, treating the point cloud as a distribution and using density estimation losses like Sliced Wasserstein to train the TT decomposition. They also exploit the inherent hierarchical structure within TT point clouds to facilitate efficient approximate nearest-neighbor searches.
The paper demonstrates the effectiveness of TT decomposition for point cloud compression and approximate nearest-neighbor search, offering a promising alternative to traditional methods, particularly in memory-constrained settings.
This research contributes to the field of large-scale machine learning by providing an efficient and scalable method for representing and searching high-dimensional data, which is crucial for various applications like image retrieval, anomaly detection, and recommendation systems.
The authors acknowledge the need for further optimization and benchmarking of the proposed ANN search method against state-of-the-art solutions. Future research could explore the application of TT point clouds to other domains and tasks beyond those explored in this paper.
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by Georgii Novi... às arxiv.org 10-08-2024
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