Computer Vision

These models are trained on custom objects

geometric computer vision algorithms

Library for efficient large-scale stereo matching

Multiple View Geometry; Structure from Motion library & softwares

List of available software packages for geometric computer vision

Library for Visual Odometry (Stereo)

C++ parser for the OpenCV library

Computational Geometry Algorithms Library

Benchmarks for stereo and optical flow

Library for Visual Odometry (Monocular)

Structure from Motion library

Library for stereo vision

Open source software to model-genic 3D reconstructions from images

Associated website of the Autonomous Systems Lab at ETH Zurich

Structure from Motion

A Python library for probabilistic inference and estimation.

Optical flow dataset and evaluation

Dataset for stixel-based depth estimation

Dense optical flow estimation

Deep learning based optical flow estimation

Optical flow estimation using deep learning

Parallel robust optical flow estimation

Optical flow by C. Liu (based on Brox et al. and Bruhn et al.)

Parallel robust optical flow estimation

Single Image Super-resolution using Deformable Patches

Learning a Deep Convolutional Network for Image Super-Resolution

Adjusted Anchored Neighborhood Regression for Fast Super-Resolution

Single Image Super-Resolution using Transformed Self-Exemplars

Spatially variant non-blind deconvolution

Handling outliers in non-blind image deconvolution

Hyper-Laplacian priors for deconvolution

Code for learning models of natural image patches

Deep convolutional neural network for image deconvolution

Deep convolutional neural network for image deconvolution

Neural deconvolution for image restoration

Removing camera shake from a single photograph

High-quality motion deblurring from a single image

Two-phase kernel estimation for robust motion deblurring

Blur kernel estimation using the radon transform

Fast motion deblurring

Blind deconvolution using a normalized sparsity measure

Blur-kernel estimation from spectral irregularities

Efficient marginal likelihood optimization in blind deconvolution

Unnatural L0 sparse representation for natural image deblurring

Edge-based blur kernel estimation using patch priors

Blind deblurring using internal patch recurrence

Non-uniform deblurring for shaken images

Single image deblurring using motion density functions

Image deblurring using inertial measurement sensors

Fast removal of non-uniform camera shake

Planar structure completion for image inpainting

Jason Chin (University of Western Ontario)

Which paper provides the best results on standard dataset X?