Towards Learning to Complete Anything in Lidar

Figure 1. Learning to Complete Anything in Lidar. Given a sparse Lidar point cloud, CAL (Complete Anything in Lidar) localizes, reconstructs, and, optionally, recognizes objects in a zero-shot fashion. By providing a semantic class vocabulary of specific object classes at test time, CAL can be prompted to perform Semantic Scene Completion (SSC), Panoptic Scene Completion (PSC), or (amodal) 3D Object Detection. Note that CAL only takes a single Lidar scan as input; RGB images are shown for visualization purposes only.