Multiscale Point Cloud Geometry Compression
- URL: http://arxiv.org/abs/2011.03799v1
- Date: Sat, 7 Nov 2020 16:11:16 GMT
- Title: Multiscale Point Cloud Geometry Compression
- Authors: Jianqiang Wang, Dandan Ding, Zhu Li, Zhan Ma
- Abstract summary: We propose a multiscale-to-end learning framework which hierarchically reconstructs the 3D Point Cloud Geometry.
The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction.
- Score: 29.605320327889142
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent years have witnessed the growth of point cloud based applications
because of its realistic and fine-grained representation of 3D objects and
scenes. However, it is a challenging problem to compress sparse, unstructured,
and high-precision 3D points for efficient communication. In this paper,
leveraging the sparsity nature of point cloud, we propose a multiscale
end-to-end learning framework which hierarchically reconstructs the 3D Point
Cloud Geometry (PCG) via progressive re-sampling. The framework is developed on
top of a sparse convolution based autoencoder for point cloud compression and
reconstruction. For the input PCG which has only the binary occupancy
attribute, our framework translates it to a downscaled point cloud at the
bottleneck layer which possesses both geometry and associated feature
attributes. Then, the geometric occupancy is losslessly compressed using an
octree codec and the feature attributes are lossy compressed using a learned
probabilistic context model.Compared to state-of-the-art Video-based Point
Cloud Compression (V-PCC) and Geometry-based PCC (G-PCC) schemes standardized
by the Moving Picture Experts Group (MPEG), our method achieves more than 40%
and 70% BD-Rate (Bjontegaard Delta Rate) reduction, respectively. Its encoding
runtime is comparable to that of G-PCC, which is only 1.5% of V-PCC.
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