3D object detector based on Hough voting achieves great success and derives many follow-
up works. Despite constantly refreshing the detection accuracy, these works suffer from …

We present a flexible and high-performance framework, named Pyramid R-CNN, for two-
stage 3D object detection from point clouds. Current approaches generally rely on the points …

A bathtub in a library, a sink in an office, a bed in a laundry room-the counter-intuition
suggests that scene provides important prior knowledge for 3D object detection, which …

Unlike 2D object detection where all RoI features come from grid pixels, the RoI feature
extraction of 3D point cloud object detection is more diverse. In this paper, we first compare …

Anchor-free object detectors are highly efficient in performing point-based prediction without
the need for extra post-processing of anchors. However, different from the 2D grids, the 3D …

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 …

Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection,
where voting is a key step. In this paper, we propose a novel VoteNet-based 3D detector …

Though 3D object detection from point clouds has achieved rapid progress in recent years,
the lack of flexible and high-performance proposal refinement remains a great hurdle for …

Many point-based 3D detectors adopt point-feature sampling strategies to drop some points
for efficient inference. These strategies are typically based on fixed and handcrafted rules …

Abstract Since Intersection-over-Union (IoU) based optimization maintains the consistency
of the final IoU prediction metric and losses, it has been widely used in both regression and …