Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention
This work addresses efficient compression for autonomous driving and robotics applications by improving on existing methods with significant speed and efficiency gains.
The paper tackles the problem of compressing sparse LiDAR point cloud geometry by proposing a neighborhood point attention method, achieving >17% BD-rate gains for lossy compression and >14% bitrate reduction for lossless compression compared to standardized methods, with a 640x speedup in decoding runtime over state-of-the-art attention-based approaches.
Although convolutional representation of multiscale sparse tensor demonstrated its superior efficiency to accurately model the occupancy probability for the compression of geometry component of dense object point clouds, its capacity for representing sparse LiDAR point cloud geometry (PCG) was largely limited. This is because 1) fixed receptive field of the convolution cannot characterize extremely and unevenly distributed sparse LiDAR points very well; and 2) pretrained convolutions with fixed weights are insufficient to dynamically capture information conditioned on the input. This work therefore suggests the neighborhood point attention (NPA) to tackle them, where we first use k nearest neighbors (kNN) to construct adaptive local neighborhood; and then leverage the self-attention mechanism to dynamically aggregate information within this neighborhood. Such NPA is devised as a NPAFormer to best exploit cross-scale and same-scale correlations for geometric occupancy probability estimation. Compared with the anchor using standardized G-PCC, our method provides >17% BD-rate gains for lossy compression, and >14% bitrate reduction for lossless scenario using popular LiDAR point clouds in SemanticKITTI and Ford datasets. Compared with the state-of-the-art (SOTA) solution using attention optimized octree coding method, our approach requires much less decoding runtime with about 640 times speedup on average, while still presenting better compression efficiency.