RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and
  Comfortable Autonomous Driving
Tong Zhao Chenfeng Xu Mingyu Ding Masayoshi Tomizuka Wei Zhan Yintao Wei
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/