Stereo Depth Estimation
46 papers with code • 5 benchmarks • 5 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
VBR
Most implemented papers
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction.
Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping Stereo
matsuren/octDPSNet
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IEEE Access 2019
Inspired by octave convolution, we divide image features into high and low spatial frequency features, and two cost volumes are generated from these using our proposed plane-sweeping module.
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume
Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images.
Normal Assisted Stereo Depth Estimation
udaykusupati/Normal-Assisted-Stereo
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CVPR 2020
We couple the learning of a multi-view normal estimation module and a multi-view depth estimation module.
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation
daiszh/A-TVSNet
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We propose a learning-based network for depth map estimation from multi-view stereo (MVS) images.
Bi3D: Stereo Depth Estimation via Binary Classifications
NVlabs/Bi3D
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CVPR 2020
Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels.
Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve
In contrast, generic camera models allow for very accurate calibration due to their flexibility.
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees
Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems.
Wasserstein Distances for Stereo Disparity Estimation
Div99/W-Stereo-Disp
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NeurIPS 2020
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
Hierarchical Neural Architecture Search for Deep Stereo Matching
XuelianCheng/LEAStereo
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NeurIPS 2020
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.
