AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

2026-03-15

AI Summary

This work addresses the significant performance degradation of existing RGB-LiDAR fusion methods for depth completion under adverse weather conditions. To this end, we propose the first unified framework for depth completion and denoising tailored to real-world complex weather scenarios, along with AURORA-KITTI—a large-scale, multimodal, multi-weather benchmark comprising 82K samples annotated with temporal metadata, textual descriptions, and occlusion labels. Building upon a distilled DDCD baseline, our approach leverages a deep foundation model to provide clean structural priors and integrates multimodal fusion with a weather-aware training strategy. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both the AURORA-KITTI and real-world DENSE datasets, exhibiting strong robustness and computational efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 multi-modal, multi-weather benchmark for robust depth completion in the wild. We further formulate Depth Completion and Denoising (DCD) as a unified task that jointly reconstructs a dense depth map from corrupted sparse inputs while suppressing weather-induced noise. AURORA-KITTI contains over \textit{82K} weather-consistent RGBL pairs with metric depth ground truth, spanning diverse weather types, three severity levels, day and night scenes, paired clean references, lens occlusion conditions, and textual descriptions. Moreover, we introduce DDCD, an efficient distillation-based baseline that leverages depth foundation models to inject clean structural priors into in-the-wild DCD training. DDCD achieves state-of-the-art performance on AURORA-KITTI and the real-world DENSE dataset while maintaining efficiency. Notably, our results further show that weather-aware, physically consistent data contributes more to robustness than architectural modifications alone. Data and code will be released upon publication.

Problem

• depth completion
• adverse weather
• LiDAR
• RGB-LiDAR fusion
• robustness

Innovation

• depth completion
• adverse weather robustness
• RGB-LiDAR fusion
• depth denoising
• foundation model distillation