发表机构
Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自动驾驶中全天候自监督深度估计问题，提出用多教师蒸馏和鲁棒雷达融合的自训练管道，包括不确定性感知多教师蒸馏及POV-BEV雷达融合方法，经实验验证该方法具鲁棒性且性能达最优。
AI 中文摘要
自监督深度估计在各种恶劣天气条件下对安全自动驾驶具有挑战性，因为传感器感知能力下降。挑战主要来自两方面：恶劣条件会扭曲像素对应关系并违反自监督损失函数中的假设，导致深度预测错误；雷达虽广泛用于恶劣天气，但稀疏分布的雷达点给自监督融合带来挑战。为解决这些问题，我们引入一种新颖的自训练管道，通过多教师蒸馏和鲁棒雷达融合使用未配对的真实全天候数据。我们提出不确定性感知多教师蒸馏方法生成不同的教师模型，并用不确定性建模权衡知识蒸馏损失。此外，设计了POV-BEV雷达融合方法，利用相机像素射线约束在相机视角和雷达鸟瞰图之间建立连接，有效利用更密集的雷达点，捕捉互补视角。大量定量和定性实验证明了该方法在全天候数据集上的鲁棒性，实现了当前最优性能。代码和模型可获取。
英文摘要
Self-supervised depth estimation is challenging for safe autonomous driving under various adverse weather conditions due to sensor perception degradation. These challenges arise from two main aspects. Firstly, adverse conditions can distort pixel correspondences and violate the assumptions embedded in the self-supervised loss function, leading to erroneous depth predictions. Secondly, while radar is a widely adopted sensor in adverse weather conditions, the sparse distribution of radar points in the Point of View (POV) poses challenges for self-supervised fusion. To address these issues, we introduce a novel self-training pipeline using unpaired real all-weather data through multi-teacher distillation and robust radar fusion. We propose the Uncertainty-Aware Multi-Teacher Distillation method to generate diverse teacher models with different adverse condition inputs, and then employ uncertainty modeling to weigh the knowledge distillation loss. Additionally, we design the POV-BEV Radar Fusion approach, which leverages camera-pixel ray constraints to establish connections between the camera's Point of View (POV) and the radar's Bird's-Eye View (BEV). This approach enables the utilization of denser radar points, effectively capturing the complementary perspectives of both POV and BEV. Extensive quantitative and qualitative experiments demonstrate the robustness of our proposed method on all-weather datasets, achieving state-of-the-art performance. Our code and models are available at https://github.com/MICLAB-BUPT/RobustDepth.
