  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.
