3D Bird's-Eye-View Instance Segmentation

Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is currently less explored. In this work, we present 3D-BEVIS (3D bird's-eye-view instance segmentation), a deep learning framework for joint semantic- and instance-segmentation on 3D point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods process local sub-parts of a full scene independently, followed by a heuristic merging step. However, to perform instance segmentation by clustering on a full scene, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information using an intermediate bird's-eye view representation.