3 citations · 6 across the 22 of their papers we have counts for
9 papers · 1 filter
StereoFactory: A Unified Merging Framework for Robust Stereo Matching
Xianda Guo, Pinhan Fu, Ruilin Wang +3
Stereo matching has advanced through foundation models trained on large-scale datasets, yet this paradigm suffers from a scalability bottleneck: incorporating new data requires cos…
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…
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…
InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving
Ruiqi Song, Xianda Guo, Yanlun Peng +3
Conventional end-to-end autonomous driving methods often rely on explicit global scene representations, which typically consist of 3D object detection, online mapping, and motion p…
WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation
Dujun Nie, Xianda Guo, Yiqun Duan +2
Object Goal Navigation-requiring an agent to locate a specific object in an unseen environment-remains a core challenge in embodied AI. Although recent progress in Vision-Language…
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…