  3D instance segmentation, with a variety of applications in robotics and
augmented reality, is in large demands these days. Unlike 2D images that are
projective observations of the environment, 3D models provide metric
reconstruction of the scenes without occlusion or scale ambiguity. In this
paper, we define "3D occupancy size", as the number of voxels occupied by each
instance. It owns advantages of robustness in prediction, on which basis,
OccuSeg, an occupancy-aware 3D instance segmentation scheme is proposed. Our
multi-task learning produces both occupancy signal and embedding
representations, where the training of spatial and feature embeddings varies
with their difference in scale-aware. Our clustering scheme benefits from the
reliable comparison between the predicted occupancy size and the clustered
occupancy size, which encourages hard samples being correctly clustered and
avoids over segmentation. The proposed approach achieves state-of-the-art
performance on 3 real-world datasets, i.e. ScanNetV2, S3DIS and SceneNN, while
maintaining high efficiency.
