  We introduce a highly performant 3D object detector for point clouds using
the DETR framework. The prior attempts all end up with suboptimal results
because they fail to learn accurate inductive biases from the limited scale of
training data. In particular, the queries often attend to points that are far
away from the target objects, violating the locality principle in object
detection. To address the limitation, we introduce a novel 3D Vertex Relative
Position Encoding (3DV-RPE) method which computes position encoding for each
point based on its relative position to the 3D boxes predicted by the queries
in each decoder layer, thus providing clear information to guide the model to
focus on points near the objects, in accordance with the principle of locality.
In addition, we systematically improve the pipeline from various aspects such
as data normalization based on our understanding of the task. We show
exceptional results on the challenging ScanNetV2 benchmark, achieving
significant improvements over the previous 3DETR in
$\rm{AP}_{25}$/$\rm{AP}_{50}$ from 65.0\%/47.0\% to 77.8\%/66.0\%,
respectively. In addition, our method sets a new record on ScanNetV2 and SUN
RGB-D datasets.Code will be released at http://github.com/yichaoshen-MS/V-DETR.
