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In point cloud compression, sufficient contexts are significant for modelingthe point cloud distribution. However, the contexts gathered by the previousvoxel-based methods decrease when handling sparse point clouds. To address thisproblem, we propose a multiple-contexts deep learning framework calledOctAttention employing the octree structure, a memory-efficient representationfor point clouds. Our approach encodes octree symbol sequences in a losslessway by gathering the information of sibling and ancestor nodes. Expressly, wefirst represent point clouds with octree to reduce spatial redundancy, which isrobust for point clouds with different resolutions. We then design aconditional entropy model with a large receptive field that models the siblingand ancestor contexts to exploit the strong dependency among the neighboringnodes and employ an attention mechanism to emphasize the correlated nodes inthe context. Furthermore, we introduce a mask operation during training andtesting to make a trade-off between encoding time and performance. Compared tothe previous state-of-the-art works, our approach obtains a 10%-35% BD-Rategain 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-basedbaseline. The code is available at https://github.com/zb12138/OctAttention.

OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression