Towards Learning to Complete Anything in Lidar
Ayça Takmaz, Cristiano Saltori, Neehar Peri, Tim Meinhardt, Riccardo de Lutio, Laura Leal-Taixé, Aljosa Osep
Abstract
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.
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Install the CLIlune papers fulltext 3a6ea2b0-8e93-43da-97f9-838939d7f487Cited by top-tier papers2
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