LOGen: Toward Lidar Object Generation by Point Diffusion

• URL: http://arxiv.org/abs/2412.07385v2
• Date: Mon, 10 Mar 2025 13:15:45 GMT
• Title: LOGen: Toward Lidar Object Generation by Point Diffusion
• Authors: Ellington Kirby, Mickael Chen, Renaud Marlet, Nermin Samet,
• Abstract summary: We introduce a novel task: LiDAR object generation, requiring models to produce 3D objects as viewed by a LiDAR scan.<n>We introduce a novel diffusion-based model to produce LiDAR point clouds of dataset objects, including intensity, and with an extensive control of the generation via conditioning information.<n>Our experiments on nuScenes show the quality of our generations measured with new 3D metrics developed to suit LiDAR objects.
• Score: 10.002129602976085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The generation of LiDAR scans is a growing area of research with diverse applications to autonomous driving. However, scan generation remains challenging, especially when compared to the rapid advancement of 2D and 3D object generation. We introduce a novel task: LiDAR object generation, requiring models to produce 3D objects as viewed by a LiDAR scan. This task focuses LiDAR scan generation on the most interesting aspect of scenes, the objects, while also benefiting from advancements in 3D object generative methods. We introduce a novel diffusion-based model to produce LiDAR point clouds of dataset objects, including intensity, and with an extensive control of the generation via conditioning information. Our experiments on nuScenes show the quality of our generations measured with new 3D metrics developed to suit LiDAR objects.

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