RoboBEV：面向损坏场景的鲁棒鸟瞰图感知
RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions
AI总结：
本文提出RoboBEV基准，系统评估BEV感知模型在八种损坏场景下的鲁棒性，发现预训练、无深度BEV变换和长时序信息有助于提升分布外鲁棒性。
摘要：
近年来，基于相机的鸟瞰图（BEV）表示取得了显著进展，展现出在车载3D感知中的巨大潜力。尽管在标准基准上已取得实质性进步，但BEV算法的鲁棒性尚未得到充分检验，而这对于安全运行至关重要。为弥补这一不足，我们提出了RoboBEV，一个全面的基准测试套件，涵盖八种不同的损坏类型，包括Bright、Dark、Fog、Snow、Motion Blur、Color Quant、Camera Crash和Frame Lost。基于该基准，我们对广泛的基于BEV的模型进行了大规模评估，以了解其韧性和可靠性。我们的研究结果表明，分布内数据集与分布外数据集上的绝对性能之间存在强相关性。然而，不同方法之间的相对性能存在显著差异。我们的实验进一步证明，预训练和无深度的BEV变换有潜力增强分布外鲁棒性。此外，利用长时且丰富的时序信息在很大程度上也有助于提升鲁棒性。我们的发现为设计未来能够在真实世界部署中同时实现准确性和鲁棒性的BEV模型提供了有价值的见解。
英文摘要：
The recent advances in camera-based bird's eye view (BEV) representation exhibit great potential for in-vehicle 3D perception. Despite the substantial progress achieved on standard benchmarks, the robustness of BEV algorithms has not been thoroughly examined, which is critical for safe operations. To bridge this gap, we introduce RoboBEV, a comprehensive benchmark suite that encompasses eight distinct corruptions, including Bright, Dark, Fog, Snow, Motion Blur, Color Quant, Camera Crash, and Frame Lost. Based on it, we undertake extensive evaluations across a wide range of BEV-based models to understand their resilience and reliability. Our findings indicate a strong correlation between absolute performance on in-distribution and out-of-distribution datasets. Nonetheless, there are considerable variations in relative performance across different approaches. Our experiments further demonstrate that pre-training and depth-free BEV transformation has the potential to enhance out-of-distribution robustness. Additionally, utilizing long and rich temporal information largely helps with robustness. Our findings provide valuable insights for designing future BEV models that can achieve both accuracy and robustness in real-world deployments.
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。
