Point Cloud Compression with Sibling Context and Surface Priors
This work addresses efficient compression of large-scale point clouds for applications like autonomous driving, representing an incremental advance with specific performance gains.
The paper tackles point cloud compression by introducing an octree-based framework with a novel entropy model that uses sibling, ancestor, and neighbor contexts along with geometric priors from quadratic surfaces, achieving bitrate improvements of 11-16% on KITTI Odometry and 12-14% on nuScenes datasets compared to state-of-the-art methods.
We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree using the context of siblings' children, ancestors, and neighbors to encode the occupancy information of each non-leaf octree node into a bitstream. Moreover, we locally fit quadratic surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding. These strong priors empower our entropy framework to encode the octree into a more compact bitstream. In the decoding stage, we apply a two-step heuristic strategy to restore point clouds with better reconstruction quality. The quantitative evaluation shows that our method outperforms state-of-the-art baselines with a bitrate improvement of 11-16% and 12-14% on the KITTI Odometry and nuScenes datasets, respectively.