  Current 3D object detection methods are heavily influenced by 2D detectors.
In order to leverage architectures in 2D detectors, they often convert 3D point
clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or
rely on detection in 2D images to propose 3D boxes. Few works have attempted to
directly detect objects in point clouds. In this work, we return to first
principles to construct a 3D detection pipeline for point cloud data and as
generic as possible. However, due to the sparse nature of the data -- samples
from 2D manifolds in 3D space -- we face a major challenge when directly
predicting bounding box parameters from scene points: a 3D object centroid can
be far from any surface point thus hard to regress accurately in one step. To
address the challenge, we propose VoteNet, an end-to-end 3D object detection
network based on a synergy of deep point set networks and Hough voting. Our
model achieves state-of-the-art 3D detection on two large datasets of real 3D
scans, ScanNet and SUN RGB-D with a simple design, compact model size and high
efficiency. Remarkably, VoteNet outperforms previous methods by using purely
geometric information without relying on color images.
