We propose a novel, conceptually simple and general framework for instance seg-mentation on 3D point clouds. Our method, called3D-BoNet, follows the simpledesign philosophy of per-point multilayer perceptrons (MLPs). The frameworkdirectly regresses 3Dboundingboxes for all instances in a point cloud, whilesimultaneously predicting a point-level mask for each instance. It consists of abackbone network followed by two parallel network branches for 1) bounding boxregression and 2) point mask prediction. 3D-BoNet is single-stage, anchor-freeand end-to-end trainable. Moreover, it is remarkably computationally efficientas, unlike existing approaches, it does not require any post-processing steps suchas non-maximum suppression, feature sampling, clustering or voting. Extensiveexperiments show that our approach surpasses existing work on both ScanNet andS3DIS datasets while being approximately10×more computationally efficient.Comprehensive ablation studies demonstrate the effectiveness of our design.