Stereo Matching Hand
35 papers with code • 0 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Stereo Matching Hand
Middlebury 2005
CATS
Middlebury 2006
Middlebury 2001
Most implemented papers
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions.
Road surface 3d reconstruction based on dense subpixel disparity map estimation
To achieve the millimetre accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used.
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
xy-guo/Learning-Monocular-Depth-by-Stereo
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ECCV 2018
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving.
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Our method is evaluated on both real-istic and synthetic stereo image pairs, and produces supe-rior results compared to the calibrated rectification or otherself-rectification approaches
Learning Depth with Convolutional Spatial Propagation Network
XinJCheng/CSPN
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In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for various depth estimation tasks.
DeepV2D: Video to Depth with Differentiable Structure from Motion
princeton-vl/DeepV2D
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ICLR 2020
We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video.
Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize
rish-av/gan_spectral_matching
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Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any supervision in the form of ground truth disparity or depth.
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event cameras
Although event-based cameras are already commercially available.
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
zswang666/Stereo-LiDAR-CCVNorm
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The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception.
Learning monocular depth estimation infusing traditional stereo knowledge
fabiotosi92/monoResMatch-Tensorflow
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CVPR 2019
Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications.
