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
this https URL .

本文提出了一系列改进点云压缩的方法，包括使用尺度先验模型进行熵编码、采用更深的变换、不同的 focal loss 权重、最优解码阈值和连续模型训练，并通过实验验证这些方法可以带来较好的 BD-PSNR 提升。