3 citations · 9 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
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…
AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion
Yuting Xie, Xianda Guo, Cong Wang +2
Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing…
Instruct Large Language Models to Drive like Humans
Ruijun Zhang, Xianda Guo, Wenzhao Zheng +3
Motion planning in complex scenarios is the core challenge in autonomous driving. Conventional methods apply predefined rules or learn from driving data to plan the future trajecto…
LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation
Xianda Guo, Chenming Zhang, Youmin Zhang +4
We present LightStereo, a cutting-edge stereo-matching network crafted to accelerate the matching process. Departing from conventional methodologies that rely on aggregating comput…
GenAD: Generative End-to-End Autonomous Driving
Wenzhao Zheng, Ruiqi Song, Xianda Guo +2
Directly producing planning results from raw sensors has been a long-desired solution for autonomous driving and has attracted increasing attention recently. Most existing end-to-e…