Towards accurate instance segmentation in large-scale LiDAR point clouds
Binbin Xiang Torben Peters Theodora Kontogianni Frawa Vetterli Stefano Puliti Rasmus Astrup Konrad Schindler
Panoptic segmentation is the combination of semantic and instance
segmentation: assign the points in a 3D point cloud to semantic categories and
partition them into distinct object instances. It has many obvious applications
for outdoor scene understanding, from city mapping to forest management.
Existing methods struggle to segment nearby instances of the same semantic
category, like adjacent pieces of street furniture or neighbouring trees, which
limits their usability for inventory- or management-type applications that rely
on object instances. This study explores the steps of the panoptic segmentation
pipeline concerned with clustering points into object instances, with the goal
to alleviate that bottleneck. We find that a carefully designed clustering
strategy, which leverages multiple types of learned point embeddings,
significantly improves instance segmentation. Experiments on the NPM3D urban
mobile mapping dataset and the FOR-instance forest dataset demonstrate the
effectiveness and versatility of the proposed strategy.