Sparse Convolution Based Octree Feature Propagation for Lidar Point Cloud Compression

With the advent of new 3D scanning technologies, point clouds have become a crucial way to depict real and virtual objects/scenes. Point clouds represent the continuous sur-faces of underlying object/scene through a collection (usually millions) of discrete, irregular, and often sparsely distributed 3D samples on the surface of the objects, e.g. LiDAR scans. This nature of point cloud data presents a considerable challenge to not only store but also understand and extract the topology of object(s) from the point cloud data. In this regard, our work presents a point cloud compression procedure that leverages sparse 3D convolutions to extract features at various octree scales for lossless compression of octree representation of point clouds. For hierarchical flow of information between octree levels, our proposed method named SparseContextNet (SCN) also propagates features from a lower resolution scale to higher resolution scale via 3D upsampling convolutions. Our experiments with LiDAR datasets reveal competitive performance of our proposal compared to the state-of-the-art.