  3D object detection received increasing attention in autonomous driving
recently. Objects in 3D scenes are distributed with diverse orientations.
Ordinary detectors do not explicitly model the variations of rotation and
reflection transformations. Consequently, large networks and extensive data
augmentation are required for robust detection. Recent equivariant networks
explicitly model the transformation variations by applying shared networks on
multiple transformed point clouds, showing great potential in object geometry
modeling. However, it is difficult to apply such networks to 3D object
detection in autonomous driving due to its large computation cost and slow
reasoning speed. In this work, we present TED, an efficient
Transformation-Equivariant 3D Detector to overcome the computation cost and
speed issues. TED first applies a sparse convolution backbone to extract
multi-channel transformation-equivariant voxel features; and then aligns and
aggregates these equivariant features into lightweight and compact
representations for high-performance 3D object detection. On the highly
competitive KITTI 3D car detection leaderboard, TED ranked 1st among all
submissions with competitive efficiency.
