  A unified neural network structure is presented for joint 3D object detection
and point cloud segmentation in this paper. We leverage rich supervision from
both detection and segmentation labels rather than using just one of them. In
addition, an extension based on single-stage object detectors is proposed based
on the implicit function widely used in 3D scene and object understanding. The
extension branch takes the final feature map from the object detection module
as input, and produces an implicit function that generates semantic
distribution for each point for its corresponding voxel center. We demonstrated
the performance of our structure on nuScenes-lidarseg, a large-scale outdoor
dataset. Our solution achieves competitive results against state-of-the-art
methods in both 3D object detection and point cloud segmentation with little
additional computation load compared with object detection solutions. The
capability of efficient weakly supervision semantic segmentation of the
proposed method is also validated by experiments.
