Learning Compact Representations for LiDAR Completion and Generation
Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, Raquel Urtasun
Abstract
Figure 1. UltraLiDAR learns discrete representations from large-scale LiDAR point clouds and conduct realistic, scalable and controllable LiDAR completion and generation. Top row: Sparse-to-dense LiDAR completion; Second row: Controllable manipulation of real LiDAR with actor removal and insertion; Third row: Diverse LiDAR generation with realistic global structure and fine-grained details; Bottom row: Conditional scene generation with partially observed point clouds (highlighted in red). Please see supp. for more examples.
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Install the CLIlune papers fulltext 80e6828c-c507-43f6-95de-5897e0a7b8b2Cited by top-tier papers33
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- Scaling Diffusion Models to Real-World 3D LiDAR Scene CompletionLucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch, Jens Behley et al.CVPR 2024 · 21 citations
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- Voxel Proposal Network via Multi-Frame Knowledge Distillation for Semantic Scene CompletionLubo Wang, Di Lin, Kairui Yang, Ruonan Liu et al.NeurIPS 2024 · 14 citations
Builds on20
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- An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn et al.ICLR 2021 · 21,477 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR SequencesJens Behley, Martin Garbade, Andres Milioto, Jan Quenzel et al.ICCV 2019 · 2,345 citations
- GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisKatja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas GeigerNeurIPS 2020 · 1,001 citations
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