A texture generator for feature-point rich textures
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Updated
Mar 2, 2017 - C++
A texture generator for feature-point rich textures
This is my personal implementation of some functions seen during the course of artificial vision. Basically, in this code are been re-implemented some of the very basic functions of OpenCV
MVL Stereo Toolbox
OpenCV C++ Stereo Tracking System
This library provides an easy and accessible way to read and write .pfm files in C++ by only relying on the sandard library.
Disparity image creation with naive and dynamic programming approaches and 3D pointcloud generation from the disparity image
Depth Estimation for Embedded Devices using USB3.0 stereo camera
Finds the stereo disparity between a pair of stereo images using SIFT and ORB algorithms
Collection of Computer Vision Algorithms
Poisson Disk Sampling with Randomized Satellite Points for Projected Texture Stereo
MVL Stereo Video Processor
CUDA belief propagation as presented in paper "GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction" published at the 2008 IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008). Code has been updated to work on current NVIDIA GPUs and with additional optimizations. Cite paper if using this code.
Python version of libelas: library for Efficient Large-scale Stereo Matching
Implements the PatchMatch stereo algorihm. AVX2 intrinsics for Intel.
Given a scene composed of mesh instances and a pair of reference view and source view, this project generates pixelwise correspondence between these views.
libStereoMatch is a universal stereo matching library. This library is developed using C++and divides the stereo matching steps into four steps: cost calculation, cost aggregation, disparity calculation, and disparity optimization. Users can build their own stereo matching algorithms through combination.
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