用深度引导多视角曝光融合，实现机器人在极端光照下的单帧高动态成像。
Depth-guided Multi-view Exposure Bracketing for HDR Robot Vision
- 基于深度信息指导多视角相机的差异曝光分配
- 在121个真实场景与20个仿真视频上表现优异
- 适合多摄像头+深度传感器的机器人视觉系统
在极端光照条件下实现可靠的单帧高动态范围（HDR）成像仍是长期挑战，且目前缺乏针对多传感器机器人系统的HDR感知评估基准。为填补这一空白，我们通过定制机器人视觉平台和iPhone 13 Pro采集了一个大规模数据集：涵盖121个真实场景（涵盖中等至超高动态范围）以及20个来自CARLA模拟器的合成视频序列。作为该数据集的参考流水线，我们提出深度引导的多视角曝光分组（DMEB），一种单帧HDR方法，它将差异极大的曝光值分配给多视角低比特深度相机，并通过基于深度的置信度感知融合进行图像融合。在该数据集上的评估表明，DMEB建立了强有力的基准，凸显了这种传感器配置在多样化多相机与深度传感器系统中实现鲁棒HDR感知的潜力。
原文摘要 · Abstract (English)
Achieving reliable single-shot high dynamic range (HDR) imaging under extreme illumination conditions remains a long-standing challenge, yet no comprehensive benchmark exist for evaluating HDR perception in multi-sensor robotic systems. To fill this gap, we introduce a large-scale dataset collected via a custom robotic vision platform and an iPhone 13 Pro: 121 real-world scenes spanning modest and ultra-high dynamic range conditions, alongside 20 synthetic video sequences from the CARLA simulator. As a reference pipeline for this dataset, we propose Depth-guided Multi-view Exposure Bracketing (DMEB), a single-shot HDR method that distributes drastically different exposures across multi-view low-bit-depth cameras and fuses them via depth-guided confidence-aware fusion. Evaluations on our dataset show that DMEB establishes a strong reference point and highlight the promise of this sensor configuration for robust HDR perception in diverse multi-camera and depth sensor system.
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