MixSup achieves strong performance with a few accurate box labels and cheap cluster labels. PointSAM is developed to generate cluster labels. It achieve on-par performance with SoTA 3D segmentation methods in nuScenes without any 3D annotations!

Drive-WM is the first multi-view world model for planning in autonomous driving.

FSDv2 is an improved version of FSD, removing the handcrafted heuristics in FSD. FSDv2 achieves strong performance in Waymo, nuScenes, and Argoverse 2 dataset, and are fully open-sourced!

CTRL is the first open-sourced LiDAR-based 3D object autolabeling system, surpassing the performance of human annotators!

FSF explores multi-modal 3D object detection with fully sparse architecture by seamlessly integrating 2D instance segmentation and 3D instance segmentaion in a unified framework.

FSD++ extends FSD into the multi-frame setting. In addition to the spatial sparsity, FSD++ emphaiszes temporal sparsity.

FSD first proposes the concept of LiDAR-based "fully sparse detection", achieving state-of-the-art performance in both conventional benchmark and long-range (>200m) LiDAR detection benchmark.

SST emphasize the small object sizes and sparsity of point clouds. Its sparse transformers enlight new backbones for outdoor LiDAR-based detection.

RangeDet greatly narrows the performance gap between range view based LiDAR detection and voxel/BEV based LiDAR detection.