  We present 3D-MPA, a method for instance segmentation on 3D point clouds.
Given an input point cloud, we propose an object-centric approach where each
point votes for its object center. We sample object proposals from the
predicted object centers. Then, we learn proposal features from grouped point
features that voted for the same object center. A graph convolutional network
introduces inter-proposal relations, providing higher-level feature learning in
addition to the lower-level point features. Each proposal comprises a semantic
label, a set of associated points over which we define a foreground-background
mask, an objectness score and aggregation features. Previous works usually
perform non-maximum-suppression (NMS) over proposals to obtain the final object
detections or semantic instances. However, NMS can discard potentially correct
predictions. Instead, our approach keeps all proposals and groups them together
based on the learned aggregation features. We show that grouping proposals
improves over NMS and outperforms previous state-of-the-art methods on the
tasks of 3D object detection and semantic instance segmentation on the
ScanNetV2 benchmark and the S3DIS dataset.

Computer Vision and Pattern Recognition