5 papers
ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving
Xianda Guo, Ruijun Zhang, Yiqun Duan +9
Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environments. Existing depth datasets su…
Stereo Anything: Unifying Zero-shot Stereo Matching with Large-Scale Mixed Data
Xianda Guo, Chenming Zhang, Youmin Zhang +8
Stereo matching serves as a cornerstone in 3D vision, aiming to establish pixel-wise correspondences between stereo image pairs for depth recovery. Despite remarkable progress driv…
StereoCarla: A High-Fidelity Driving Dataset for Generalizable Stereo
Xianda Guo, Chenming Zhang, Ruilin Wang +6
Stereo matching plays a crucial role in enabling depth perception for autonomous driving and robotics. While recent years have witnessed remarkable progress in stereo matching algo…
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
Anqing Jiang, Yu Gao, Zhigang Sun +11
Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which ena…
SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models
Xianda Guo, Ruijun Zhang, Yiqun Duan +7
Accurate spatial reasoning in outdoor environments - covering geometry, object pose, and inter-object relationships - is fundamental to downstream tasks such as mapping, motion for…