Stereo Matching Hand
35 papers with code • 0 benchmarks • 6 datasets
Benchmarks
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Middlebury 2005
CATS
Middlebury 2006
Middlebury 2001
Most implemented papers
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.
Binary Stereo Matching
In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem.
Cross-Scale Cost Aggregation for Stereo Matching
We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels.
Computing the Stereo Matching Cost with a Convolutional Neural Network
We present a method for extracting depth information from a rectified image pair.
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling
Instead, we propose a generic architecture that decomposes the label improvement task to three steps: 1) detecting the initial label estimates that are incorrect, 2) replacing the incorrect labels with new ones, and finally 3) refining the renewed labels by predicting residual corrections w. r. t.
Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning
amitshaked/resmatch
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CVPR 2017
We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches.
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement.
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts.
Single View Stereo Matching
The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data.
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details.
