Surface-Constrained Progressive Feature Preserving Point Cloud Compression

Abstract

Current point cloud compression methods based on deep learning cannot guarantee that the reconstructed points are constrained to the surface, resulting in low reconstruction quality at low bitrates. Hence, this paper proposes an efficient deep learning-based point cloud geometry compression algorithm. Specifically, by introducing a two-dimensional plane at the decoder, the reconstructed local patch is constrained within a manifold, preserving sufficient surface features. This strategy ensures the decoder can reconstruct high-quality point clouds even at low bitrates. Moreover, we use the anchor features obtained by the neural network to compress the local features at the encoder. The experimental results show that, under the condition of the same restoration quality, the proposed method improves the point-to-plane PSNR by more than 2dB compared to the state-of-the-art methods. The code is available at https://github.com/zbaoye/SurfPCC.