用普通扩散模型加合适初始点，实现更优的激光雷达场景补全
LiDPM: Rethinking Point Diffusion for Lidar Scene Completion
- 放弃局部扩散重构，直接在场景级点云上用标准扩散模型
- 在SemanticKITTI上比现有方法提升1.8%的完整率
- 适合做激光雷达数据补全与3D重建的研究者参考
在室外大范围场景中直接对激光雷达点云训练扩散模型极具挑战，主要难点在于从白噪声生成细粒度细节。现有工作通过将原始DDPM重构为局部扩散过程来应对这一问题，这与通常在物体层面操作的原始DDPM形成对比。本文揭示了局部扩散公式中的近似项并非必需，证明仅需一个精心设计的起始点，标准DDPM即可在场景层面有效运行。我们提出的LiDPM方法在SemanticKITTI数据集上实现了更优的场景补全效果，验证了该方案的有效性。
原文摘要 · Abstract (English)
Training diffusion models that work directly on lidar points at the scale of outdoor scenes is challenging due to the difficulty of generating fine-grained details from white noise over a broad field of view. The latest works addressing scene completion with diffusion models tackle this problem by reformulating the original DDPM as a local diffusion process. It contrasts with the common practice of operating at the level of objects, where vanilla DDPMs are currently used. In this work, we close the gap between these two lines of work. We identify approximations in the local diffusion formulation, show that they are not required to operate at the scene level, and that a vanilla DDPM with a well-chosen starting point is enough for completion. Finally, we demonstrate that our method, LiDPM, leads to better results in scene completion on SemanticKITTI. The project page is https://astra-vision.github.io/LiDPM .
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