Disparity Estimation

37 papers with code • 4 benchmarks • 3 datasets

The Disparity Estimation is the task of finding the pixels in the multiscopic views that correspond to the same 3D point in the scene.

Most implemented papers

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

HKBU-HPML/FADNet CVPR 2016

By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching

cogsys-tuebingen/mobilestereonet 22 Aug 2021

Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively.

Learning for Disparity Estimation through Feature Constancy

JiaRenChang/PSMNet CVPR 2018

The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.

Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

DeepMotionAIResearch/DenseMatchingBenchmark 9 Sep 2019

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.

Superpixel Segmentation with Fully Convolutional Networks

fuy34/superpixel_fcn CVPR 2020

In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.

CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

gallenszl/CFNet CVPR 2021

In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network.

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

dhernandez0/sgm 13 Oct 2016

Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles.

Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling

gidariss/DRR_struct_pred CVPR 2017

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.

CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation

kbatsos/CBMV CVPR 2018

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.

Variational Disparity Estimation Framework for Plenoptic Image

hieuttcse/variational_plenoptic_disparity_estimation 18 Apr 2018

This paper presents a computational framework for accurately estimating the disparity map of plenoptic images.