GEM is a new LiDAR world model using deformable Mamba that disentangles dynamic and static features to generate high-fidelity simulations and achieve state-of-the-art results on autonomous driving benchmarks.
Pointnet: Deep learning on point sets for 3d classification and segmentation
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DEC combines a DINO backbone, a Chunking and Adapting Module, and CLIP-driven virtual feature synthesis to improve open-set 3D object retrieval on standard benchmarks.
citing papers explorer
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GEM: Generating LiDAR World Model via Deformable Mamba
GEM is a new LiDAR world model using deformable Mamba that disentangles dynamic and static features to generate high-fidelity simulations and achieve state-of-the-art results on autonomous driving benchmarks.
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DINO Eats CLIP: Adapting Beyond Knowns for Open-set 3D Object Retrieval
DEC combines a DINO backbone, a Chunking and Adapting Module, and CLIP-driven virtual feature synthesis to improve open-set 3D object retrieval on standard benchmarks.