  3D object detection algorithms for autonomous driving reason about 3D
obstacles either from 3D birds-eye view or perspective view or both. Recent
works attempt to improve the detection performance via mining and fusing from
multiple egocentric views. Although the egocentric perspective view alleviates
some weaknesses of the birds-eye view, the sectored grid partition becomes so
coarse in the distance that the targets and surrounding context mix together,
which makes the features less discriminative. In this paper, we generalize the
research on 3D multi-view learning and propose a novel multi-view-based 3D
detection method, named X-view, to overcome the drawbacks of the multi-view
methods. Specifically, X-view breaks through the traditional limitation about
the perspective view whose original point must be consistent with the 3D
Cartesian coordinate. X-view is designed as a general paradigm that can be
applied on almost any 3D detectors based on LiDAR with only little increment of
running time, no matter it is voxel/grid-based or raw-point-based. We conduct
experiments on KITTI and NuScenes datasets to demonstrate the robustness and
effectiveness of our proposed X-view. The results show that X-view obtains
consistent improvements when combined with four mainstream state-of-the-art 3D
methods: SECOND, PointRCNN, Part-A^2, and PV-RCNN.
