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ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression

DOI:
10.60864/mrxb-r952
Citation Author(s):
Submitted by:
Yan Wang
Last updated:
6 June 2024 - 10:55am
Document Type:
Poster
Event:
Presenters:
Yan Wang
Paper Code:
MMSP-P1.6
 

Recently, deep learning methods have shown promising results in point cloud compression. However, previous octree-based approaches either lack sufficient context or have high decoding complexity (e.g. > 900s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a segment-constrained multi-group coding strategy to exploit the autoregressive context while maintaining decoding efficiency. Then, we propose a dual transformer architecture to utilize the dependency of current node on its ancestors and siblings. We also propose a random-masking pre-train method to enhance our model. Experimental results show that our approach achieves state-of-the-art performance for both lossy and lossless point cloud compression, and saves a significant amount of decoding time compared with previous octree-based SOTA compression methods.

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