Improved Deep Point Cloud Geometry Compression

• URL: http://arxiv.org/abs/2006.09043v2
• Date: Wed, 24 Jun 2020 12:56:23 GMT
• Title: Improved Deep Point Cloud Geometry Compression
• Authors: Maurice Quach, Giuseppe Valenzise, Frederic Dufaux
• Abstract summary: We propose a set of contributions to improve deep point cloud compression. An optimal combination of the proposed improvements achieves BD-PSNR gains over G-PCC trisoup and octree of 5.50 (6.48) dB and 6.84 (5.95) dB, respectively.
• Score: 10.936043362876651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds have been recognized as a crucial data structure for 3D content and are essential in a number of applications such as virtual and mixed reality, autonomous driving, cultural heritage, etc. In this paper, we propose a set of contributions to improve deep point cloud compression, i.e.: using a scale hyperprior model for entropy coding; employing deeper transforms; a different balancing weight in the focal loss; optimal thresholding for decoding; and sequential model training. In addition, we present an extensive ablation study on the impact of each of these factors, in order to provide a better understanding about why they improve RD performance. An optimal combination of the proposed improvements achieves BD-PSNR gains over G-PCC trisoup and octree of 5.50 (6.48) dB and 6.84 (5.95) dB, respectively, when using the point-to-point (point-to-plane) metric. Code is available at https://github.com/mauriceqch/pcc_geo_cnn_v2 .

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