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
Latest papers
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.
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.
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy
The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions.
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.
Deep Depth Estimation From Thermal Image
ukcheolshin/ms2-multispectralstereodataset
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CVPR 2023
Secondly, we conduct an exhaustive validation process of monocular and stereo depth estimation algorithms designed on visible spectrum bands to benchmark their performance in the thermal image domain.
Energy-Efficient Adaptive 3D Sensing
Active depth sensing achieves robust depth estimation but is usually limited by the sensing range.
Unifying Flow, Stereo and Depth Estimation
We present a unified formulation and model for three motion and 3D perception tasks: optical flow, rectified stereo matching and unrectified stereo depth estimation from posed images.
Context-Enhanced Stereo Transformer
guoweiyu/context-enhanced-stereo-transformer
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We construct our stereo depth estimation model, Context Enhanced Stereo Transformer (CSTR), by plugging CEP into the state-of-the-art stereo depth estimation method Stereo Transformer.
Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion
Event cameras are bio-inspired sensors that mimic the human retina by responding to brightness changes in the scene.
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques
Previous work has shown this trade-off can be improved by developing a state-of-the-art method (AnyNet) to improve stereo depth estimation.
