3D-BEVIS: Bird's-Eye-View Instance Segmentation
This work addresses instance segmentation in 3D scenes, a problem for robotics and autonomous systems, but it is incremental as it builds on existing proposal-free approaches.
The paper tackles 3D instance segmentation on point clouds by proposing 3D-BEVIS, which combines local point geometry with global context from a bird's-eye view to achieve globally consistent features for clustering, resulting in improved performance on this less-explored task.
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 less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on 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 scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation.