Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention. (arXiv:2208.12573v1 [cs.CV])

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 …

arxiv attention cloud compression cv geometry lidar