The content introduces the concept of representing dynamic 3D point cloud sequences as Structured Point Cloud Videos (SPCV) to address challenges in processing and analyzing unstructured data. The proposed method aims to enhance efficiency, effectiveness, and performance in downstream tasks such as action recognition, temporal interpolation, and compression. Extensive experiments demonstrate the superiority of SPCV in preserving spatial smoothness, temporal consistency, and geometric fidelity across frames.
The author discusses the limitations of existing deep learning approaches for processing dynamic 3D point cloud sequences due to irregularity and lack of structure. By structuring these sequences into SPCVs, the proposed method offers benefits such as improved efficiency, reduced memory consumption, simplified design, and compatibility with established 2D image/video techniques.
Key points include the development of a self-supervised learning pipeline for geometric regularized representation using SPCVs, construction of frameworks for various processing tasks like action recognition and compression based on SPCVs, and addressing challenges related to spatial smoothness and temporal consistency in dynamic point cloud sequences.
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by Yiming Zeng,... klokken arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01129.pdfDypere Spørsmål