  We introduce H3DNet, which takes a colorless 3D point cloud as input and
outputs a collection of oriented object bounding boxes (or BB) and their
semantic labels. The critical idea of H3DNet is to predict a hybrid set of
geometric primitives, i.e., BB centers, BB face centers, and BB edge centers.
We show how to convert the predicted geometric primitives into object proposals
by defining a distance function between an object and the geometric primitives.
This distance function enables continuous optimization of object proposals, and
its local minimums provide high-fidelity object proposals. H3DNet then utilizes
a matching and refinement module to classify object proposals into detected
objects and fine-tune the geometric parameters of the detected objects. The
hybrid set of geometric primitives not only provides more accurate signals for
object detection than using a single type of geometric primitives, but it also
provides an overcomplete set of constraints on the resulting 3D layout.
Therefore, H3DNet can tolerate outliers in predicted geometric primitives. Our
model achieves state-of-the-art 3D detection results on two large datasets with
real 3D scans, ScanNet and SUN RGB-D.
