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This paper addresses the growing demands for safety and comfort inintelligent robot systems, particularly autonomous vehicles, where roadconditions play a pivotal role in overall driving performance. For example,reconstructing road surfaces helps to enhance the analysis and prediction ofvehicle responses for motion planning and control systems. We introduce theRoad Surface Reconstruction Dataset (RSRD), a real-world, high-resolution, andhigh-precision dataset collected with a specialized platform in diverse drivingconditions. It covers common road types containing approximately 16,000 pairsof stereo images, original point clouds, and ground-truth depth/disparity maps,with accurate post-processing pipelines to ensure its quality. Based on RSRD,we further build a comprehensive benchmark for recovering road profiles throughdepth estimation and stereo matching. Preliminary evaluations with variousstate-of-the-art methods reveal the effectiveness of our dataset and thechallenge of the task, underscoring substantial opportunities of RSRD as avaluable resource for advancing techniques, e.g., multi-view stereo towardssafe autonomous driving. The dataset and demo videos are available athttps://thu-rsxd.com/rsrd/

RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving