OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
Stereo matching aims to estimate the disparity between matching pixels in a stereo image pair, which is important to robotics, autonomous driving, and other computer vision tasks. Despite the development of numerous impressive methods in recent years, determining the most suitable architecture for practical application remains challenging. Addressing this gap, our paper introduces a comprehensive benchmark focusing on practical applicability rather than solely on individual models for optimized performance. Specifically, we develop a flexible and efficient stereo matching codebase, called OpenStereo. OpenStereo includes training and inference codes of more than 10 network models, making it, to our knowledge, the most complete stereo matching toolbox available. Based on OpenStereo, we conducted experiments and have achieved or surpassed the performance metrics reported in the original paper. Additionally, we conduct an exhaustive analysis and deconstruction of recent developments in stereo matching through comprehensive ablative experiments. These investigations inspired the creation of StereoBase, a strong baseline model. Our StereoBase ranks 1st on SceneFlow, KITTI 2015, 2012 (Reflective) among published methods and achieves the best performance across all metrics. In addition, StereoBase has strong cross-dataset generalization. Code is available at \url{https://github.com/XiandaGuo/OpenStereo}.
Code (1)
Tasks
Autonomous DrivingAutonomous NavigationStereo MatchingSimilar Papers 제목 키워드 기반
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation
We present LightStereo, a cutting-edge stereo-matching network crafted to accelerate the matching process. Departing from conventional methodologies that rely on aggregating computationally intensive 4D costs, LightStere…
AllStereo MatchingStereoCarla: A High-Fidelity Driving Dataset for Generalizable Stereo
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 algorithms, largely driven by learning-based…
Autonomous VehiclesAutonomous DrivingStereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data
Stereo matching has been a pivotal component in 3D vision, aiming to find corresponding points between pairs of stereo images to recover depth information. In this work, we introduce StereoAnything, a highly practical so…
Stereo MatchingFoundationStereo: Zero-Shot Stereo Matching
Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. However, achieving strong zero-shot generalization - a hallmark of foundation models in other compu…
Depth EstimationDiversityStereo Depth EstimationStereo Matching+1Iterative Volume Fusion for Asymmetric Stereo Matching
Stereo matching is vital in 3D computer vision, with most algorithms assuming symmetric visual properties between binocular visions. However, the rise of asymmetric multi-camera systems (e.g., tele-wide cameras) challeng…