Kavli Affiliate: Wei Gao
| First 5 Authors: Chunyang Fu, Ge Li, Rui Song, Wei Gao, Shan Liu
| Summary:
In point cloud compression, sufficient contexts are significant for modeling
the point cloud distribution. However, the contexts gathered by the previous
voxel-based methods decrease when handling sparse point clouds. To address this
problem, we propose a multiple-contexts deep learning framework called
OctAttention employing the octree structure, a memory-efficient representation
for point clouds. Our approach encodes octree symbol sequences in a lossless
way by gathering the information of sibling and ancestor nodes. Expressly, we
first represent point clouds with octree to reduce spatial redundancy, which is
robust for point clouds with different resolutions. We then design a
conditional entropy model with a large receptive field that models the sibling
and ancestor contexts to exploit the strong dependency among the neighboring
nodes and employ an attention mechanism to emphasize the correlated nodes in
the context. Furthermore, we introduce a mask operation during training and
testing to make a trade-off between encoding time and performance. Compared to
the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate
gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset
(e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based
baseline. The code is available at https://github.com/zb12138/OctAttention.
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