DMGC:Deep Triangle Mesh Geometry Compression via Connectivity Prediction

Abstract

We propose a novel deep lossless geometry compression algorithm for triangle mesh. Typical traditional triangle mesh compression algorithms are connectivity-driven, which first codes connectivity and then codes vertices according to the encoded connectivity. However, vertex compression is inefficient since these algorithms lack the exploitation of vertices' intra-component redundancy, which point cloud compression (PCC) excels at. Therefore, our approach first compresses the vertices with a lossless PCC algorithm since the vertices could be considered as a point cloud. Moreover, based on the encoded vertices, the bitrate of connectivity could be decreased further by exploiting the cross-component redundancy between vertex and connectivity. Specifically, we divide the connectivity into KNN connectivity and isolated connectivity. We design a deep entropy model for KNN connectivity compression. This model extracts the spatial feature of encoded vertices first, then predicts the vertex-pairs' connection probabilities using the spatial feature. In order to exploit the intra-component redundancy, an auto-regressive strategy is also employed when predicting. The predicted probabilities are finally fed into a binary arithmetic coder to code the KNN connectivity into a compact bitstream. The isolated connectivity is encoded in direct coding mode (DCM). To our knowledge, this paper is the first work to utilize the deep neural network on mesh compression. We validate the effectiveness of our method on simplified MPEG V-DMC dataset. Experimental results demonstrate that the proposed method achieves average 7.33% and 40.75% bpv gains on connectivity and vertex separately, resulting average 30.26% bpv gains on total mesh compression compared with MPEG SC3DMC.