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 with no code
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks
Our network has a very large receptive field and avoids striding layers to maintain spatial resolution.
Multi-Level Context Ultra-Aggregation for Stereo Matching
We also evaluate our method on Scene Flow and KITTI 2012/2015 stereo datasets.
Local Detection of Stereo Occlusion Boundaries
Stereo occlusion boundaries are one-dimensional structures in the visual field that separate foreground regions of a scene that are visible to both eyes (binocular regions) from background regions of a scene that are visible to only one eye (monocular regions).
DISCO: Depth Inference from Stereo using Context
Recent deep learning based approaches have outperformed classical stereo matching methods.
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model
Nowadays dense stereo matching has become one of the dominant tools in 3D reconstruction of urban regions for its low cost and high flexibility in generating dense 3D points.
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics.
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e. g. autonomous driving, which needs a high degree of confidence as mandatory prerequisite.
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping
The intersection angle between two images are normally seen as the most important one in stereo data acquisition, as the state-of-the-art DIM algorithms work best on narrow baseline (smaller intersection angle) stereos (E. g. Semi-Global Matching regards 15-25 degrees as good intersection angle).
Automated 3D recovery from very high resolution multi-view satellite images
Multiple depth maps derived from individual image pairs are fused with an adaptive 3D median filter that considers the image spectral similarities.
FPGA-based Binocular Image Feature Extraction and Matching System
Image feature extraction and matching is a fundamental but computation intensive task in machine vision.
