  Existing point cloud based 3D detectors are designed for the particular
scene, either indoor or outdoor ones. Because of the substantial differences in
object distribution and point density within point clouds collected from
various environments, coupled with the intricate nature of 3D metrics, there is
still a lack of a unified network architecture that can accommodate diverse
scenes. In this paper, we propose Uni3DETR, a unified 3D detector that
addresses indoor and outdoor 3D detection within the same framework.
Specifically, we employ the detection transformer with point-voxel interaction
for object prediction, which leverages voxel features and points for
cross-attention and behaves resistant to the discrepancies from data. We then
propose the mixture of query points, which sufficiently exploits global
information for dense small-range indoor scenes and local information for
large-range sparse outdoor ones. Furthermore, our proposed decoupled IoU
provides an easy-to-optimize training target for localization by disentangling
the xy and z space. Extensive experiments validate that Uni3DETR exhibits
excellent performance consistently on both indoor and outdoor 3D detection. In
contrast to previous specialized detectors, which may perform well on some
particular datasets but suffer a substantial degradation on different scenes,
Uni3DETR demonstrates the strong generalization ability under heterogeneous
conditions (Fig. 1).
  Codes are available at
\href{https://github.com/zhenyuw16/Uni3DETR}{https://github.com/zhenyuw16/Uni3DETR}.
