RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving
This provides a domain-specific resource for improving safety and comfort in autonomous vehicles, but it is incremental as it focuses on dataset creation and benchmarking.
The paper tackles the problem of road surface reconstruction for autonomous driving by introducing the RSRD dataset, which includes approximately 16,000 stereo image pairs and ground-truth data, and preliminary evaluations show it effectively challenges existing methods.
This paper addresses the growing demands for safety and comfort in intelligent robot systems, particularly autonomous vehicles, where road conditions play a pivotal role in overall driving performance. For example, reconstructing road surfaces helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction Dataset (RSRD), a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of 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 through depth estimation and stereo matching. Preliminary evaluations with various state-of-the-art methods reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of RSRD as a valuable resource for advancing techniques, e.g., multi-view stereo towards safe autonomous driving. The dataset and demo videos are available at https://thu-rsxd.com/rsrd/