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TUM-MLS-2016
TUM-MLS-2016: An annotated mobile LiDAR dataset of the TUM City Campus for semantic point cloud interpretation in urban areas. -
ScanNet-v2
Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud instance segmentation. However, we observed that existing methods would... -
Semantic3D
A large-scale point cloud classification benchmark, focusing on semantic segmentation of urban scenes. -
ModelNet40
Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose...