  We present a novel architecture for 3D object detection, M3DeTR, which
combines different point cloud representations (raw, voxels, bird-eye view)
with different feature scales based on multi-scale feature pyramids. M3DeTR is
the first approach that unifies multiple point cloud representations, feature
scales, as well as models mutual relationships between point clouds
simultaneously using transformers. We perform extensive ablation experiments
that highlight the benefits of fusing representation and scale, and modeling
the relationships. Our method achieves state-of-the-art performance on the
KITTI 3D object detection dataset and Waymo Open Dataset. Results show that
M3DeTR improves the baseline significantly by 1.48% mAP for all classes on
Waymo Open Dataset. In particular, our approach ranks 1st on the well-known
KITTI 3D Detection Benchmark for both car and cyclist classes, and ranks 1st on
Waymo Open Dataset with single frame point cloud input. Our code is available
at: https://github.com/rayguan97/M3DETR.
