Convolutional Neural Network-Based Occupancy Map Accuracy Improvement for Video-Based Point Cloud Compression

In video-based point cloud compression (V-PCC), a dynamic point cloud is projected onto geometry and attribute videos patch by patch for compression. In addition to the geometry and attribute videos, an occupancy map video is compressed into a V-PCC bitstream to indicate whether a two-dimensional (2D) point in the projected geometry video corresponds to any point in three-dimensional (3D) space. The occupancy map video is usually downsampled before compression to obtain a tradeoff between the bitrate and the reconstructed point cloud quality. Due to the accuracy loss in the downsampling process, some noisy points are generated, which leads to severe objective and subjective quality degradation of the reconstructed point cloud. To improve the quality of the reconstructed point cloud, we propose using a convolutional neural network (CNN) to improve the accuracy of the occupancy map video. We mainly make the following contributions. First, we improve the accuracy of the occupancy map video by formulating the problem as a binary segmentation problem since the pixel values of the occupancy map video are either 0 or 1. Second, in addition to the downsampled occupancy map video, we introduce a reconstructed geometry video as the other input of the CNN to provide more useful information in order to indicate the occupancy map video. To the best of our knowledge, this is the first learning-based work to improve the performance of V-PCC. Compared to state-of-the-art schemes, our proposed CNN-based approach achieves much more accurate occupancy map videos and significant bitrate savings.

Automated summary

In video-based point cloud compression, we propose using a convolutional neural network (CNN) to improve the accuracy of the occupancy map video, resulting in more accurate occupancy map videos and significant bitrate savings.