AURORA-KITTI: 任意天气下的深度补全与去噪
AURORA-KITTI: Any-Weather Depth Completion and Denoising
AI总结：
本文提出AURORA-KITTI，首个大规模多模态多天气基准，通过统一任务深度补全与去噪，在恶劣天气下实现鲁棒的深度重建，同时引入DDCD基线模型提升效率与鲁棒性。
摘要：
鲁棒的深度补全对现实世界的3D场景理解至关重要，但现有RGB-LiDAR融合方法在恶劣天气下显著退化，因为相机图像和LiDAR测量均受天气影响而受损。本文引入AURORA-KITTI，首个大规模多模态、多天气基准，用于野外鲁棒深度补全。我们进一步将深度补全与去噪（DCD）统一为一个任务，联合从受损稀疏输入中重建密集深度图并抑制天气引起的噪声。AURORA-KITTI包含超过82K组天气一致的RGBL配对，具有度量深度地面真实值，涵盖多样化的天气类型、三种严重等级、日夜间场景、配对干净参考、镜头遮挡条件和文本描述。此外，我们引入DDCD，一种高效的基于蒸馏的基线，利用深度基础模型将干净的结构先验注入野外DCD训练。DDCD在AURORA-KITTI和现实世界DENSE数据集上实现了最先进的性能，同时保持高效。值得注意的是，我们的结果进一步表明，天气感知、物理一致的数据比仅靠架构修改更能提升鲁棒性。数据和代码将在发表后发布。
英文摘要：
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB--LiDAR fusion methods degrade significantly under adverse weather, where both camera images and LiDAR measurements suffer from weather-induced corruption. In this paper, we introduce AURORA-KITTI, the first large-scale, physics-consistent synthetic RGB-LiDAR benchmark for any-weather depth completion and denoising. We further formulate Depth Completion and Denoising~(DCD) as a unified task that reconstructs clean dense metric depth from weather-corrupted RGB images and sparse LiDAR inputs while suppressing weather-induced noise. AURORA-KITTI contains over 82K weather-consistent RGB--LiDAR pairs with metric depth supervision, spanning diverse weather types, three severity levels, day/night scenes, paired clean references, lens occlusion conditions, and textual descriptions.Moreover, we provide DDCD as a strong and efficient distillation-based reference baseline. DDCD is trained on AURORA-KITTI using a frozen teacher applied to paired clean RGB images only during training; at inference, it uses weather-corrupted RGB and sparse LiDAR. Experiments on AURORA-KITTI and the real-world DENSE benchmark show that existing depth completion and foundation depth models remain vulnerable under severe weather, while DDCD improves over weather-aware retraining. Controlled experiments further show that physics-consistent adverse-weather data is the dominant driver of robustness, with clean-prior distillation providing complementary gains. Comparisons with off-the-shelf zero-shot methods use different training protocols; same-data retraining and component ablations separately quantify the contributions of weather-aware data and DDCD. Github: https://github.com/Warwick-Jocelyn/AURORA-KITTI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
