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Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception
[article]
2021
arXiv
pre-print
State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, \etc) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the
arXiv:2109.05441v1
fatcat:gmsqejbktjbkxiot5jvjal4xei