Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

149 Citations (Scopus)
266 Downloads (Pure)


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 10× more computationally efficient. Comprehensive ablation studies demonstrate the effectiveness of our design.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 32
PublisherNeural Information Processing Systems Foundation
Number of pages10
Publication statusPublished - 2019
Event33rd Conference on Neural Information Processing Systems 2019 - Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019


Conference33rd Conference on Neural Information Processing Systems 2019
Abbreviated titleNeurIPS 2019


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