  Existing 3D semantic segmentation methods rely on point-wise or voxel-wise
feature descriptors to output segmentation predictions. However, these
descriptors are often supervised at point or voxel level, leading to
segmentation models that can behave poorly at instance-level. In this paper, we
proposed a novel instance-aware approach for 3D semantic segmentation. Our
method combines several geometry processing tasks supervised at instance-level
to promote the consistency of the learned feature representation. Specifically,
our methods use shape generators and shape classifiers to perform shape
reconstruction and classification tasks for each shape instance. This enforces
the feature representation to faithfully encode both structural and local shape
information, with an awareness of shape instances. In the experiments, our
method significantly outperform existing approaches in 3D semantic segmentation
on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and
ScanNetV2.
