Stereo Depth Estimation
46 papers with code • 5 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Stereo Depth Estimation
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Libraries
Use these libraries to find Stereo Depth Estimation models and implementationsDatasets
KITTI
Spring
GUISS dataset
Most implemented papers
Towards Continual, Online, Self-Supervised Depth
We apply our method to both structure-from-motion and stereo depth estimation.
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Stereo matching is a fundamental building block for many vision and robotics applications.
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology.
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes.
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation.
SOS: Stereo Matching in O(1) with Slanted Support Windows
Our key insight is that local smoothness can in fact be used to amortize the computation not only within initialization, but across the entire stereo pipeline.
Real-time self-adaptive deep stereo
CVLAB-Unibo/Real-time-self-adaptive-deep-stereo
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CVPR 2019
Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs.
Learning to Adapt for Stereo
CVLAB-Unibo/Learning2AdaptForStereo
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CVPR 2019
Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment.
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
baidu-research/UnDepthflow
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CVPR 2019
In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption.
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
mileyan/Pseudo_Lidar_V2
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ICLR 2020
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
