Point Cloud Compression with Sibling Context and Surface Priors
- URL: http://arxiv.org/abs/2205.00760v1
- Date: Mon, 2 May 2022 09:13:26 GMT
- Title: Point Cloud Compression with Sibling Context and Surface Priors
- Authors: Zhili Chen, Zian Qian, Sukai Wang, Qifeng Chen
- Abstract summary: We present a novel octree-based multi-level framework for large-scale point cloud compression.
In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree.
We locally fit surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding.
- Score: 47.96018990521301
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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