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
Latest papers
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
uber-research/DeepPruner
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ICCV 2019
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference.
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
DeepMotionAIResearch/DenseMatchingBenchmark
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However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.
OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
hyu-cvlab/omnimvs-pytorch
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ICCV 2019
The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information.
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization
This paper proposes a novel approach for extending monocular visual odometry to a stereo camera system.
Guided Stereo Matching
mattpoggi/guided-stereo
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CVPR 2019
Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch.
Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence
lelimite4444/BridgeDepthFlow
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CVPR 2019
In this paper, we propose a single and principled network to jointly learn spatiotemporal correspondence for stereo matching and flow estimation, with a newly designed geometric connection as the unsupervised signal for temporally adjacent stereo pairs.
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation
dfki-av/pwoc-3d
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In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching.
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.
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
AvrilCheng/LidarStereoNet
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CVPR 2019
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps.
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.
