3 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors
Luca Bartolomei, Fabio Tosi, Matteo Poggi +2
We propose EventHub, a novel framework for training deep-event stereo networks without ground truth annotations from costly active sensors, relying instead on standard color images…
FlowIt: Global Matching via Hierarchical Transformers and Optimal Transport for Optical Flow
Sadra Safadoust, Fabio Tosi, Matteo Poggi +1
We present FlowIt, a novel architecture for optical flow estimation that combines global matching with confidence and occlusion-guided refinement. At its core, FlowIt leverages a h…
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…
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…
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…