用扩散模型补全激光雷达点云的语义与几何信息。
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models
- 在点云和语义空间分别进行去噪扩散建模。
- 在自动驾驶数据集上达到当前最优的场景补全效果。
- 适合做自动驾驶感知系统中点云重建的研究者。
感知系统在自动驾驶中至关重要，依赖多传感器及计算机视觉算法。3D激光雷达常生成稀疏点云，难以感知遮挡区域和场景空隙，因点云稀疏且缺乏语义信息。为解决此问题，语义场景补全（Semantic Scene Completion, SSC）基于原始激光雷达测量，联合预测未观测到的几何结构与语义标签，以实现更完整的场景表征。受扩散模型在图像生成与超分辨率任务中的成功启发，本文首次将去噪扩散过程扩展至点云与语义空间，分别建模。通过以语义激光雷达点云作为条件输入，并设计局部与全局正则化损失，有效稳定生成过程。在多个自动驾驶数据集上的实验表明，该方法在SSC任务上达到当前最优性能，显著超越多数现有方法。
原文摘要 · Abstract (English)
Perception systems play a crucial role in autonomous driving, incorporating multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors are widely used to capture sparse point clouds of the vehicle's surroundings. However, such systems struggle to perceive occluded areas and gaps in the scene due to the sparsity of these point clouds and their lack of semantics. To address these challenges, Semantic Scene Completion (SSC) jointly predicts unobserved geometry and semantics in the scene given raw LiDAR measurements, aiming for a more complete scene representation. Building on promising results of diffusion models in image generation and super-resolution tasks, we propose their extension to SSC by implementing the noising and denoising diffusion processes in the point and semantic spaces individually. To control the generation, we employ semantic LiDAR point clouds as conditional input and design local and global regularization losses to stabilize the denoising process. We evaluate our approach on autonomous driving datasets, and it achieves state-of-the-art performance for SSC, surpassing most existing methods.
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