  We propose a novel, conceptually simple and general framework for instance
segmentation on 3D point clouds. Our method, called 3D-BoNet, follows the
simple design philosophy of per-point multilayer perceptrons (MLPs). The
framework directly regresses 3D bounding boxes for all instances in a point
cloud, while simultaneously predicting a point-level mask for each instance. It
consists of a backbone network followed by two parallel network branches for 1)
bounding box regression and 2) point mask prediction. 3D-BoNet is single-stage,
anchor-free and end-to-end trainable. Moreover, it is remarkably
computationally efficient as, unlike existing approaches, it does not require
any post-processing steps such as non-maximum suppression, feature sampling,
clustering or voting. Extensive experiments show that our approach surpasses
existing work on both ScanNet and S3DIS datasets while being approximately 10x
more computationally efficient. Comprehensive ablation studies demonstrate the
effectiveness of our design.
