Efficient Global Point-cloud registration
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Updated
Sep 28, 2021 - C++
Efficient Global Point-cloud registration
A lean C++ library for working with point cloud data
The Graph-Cut RANSAC algorithm proposed in paper: Daniel Barath and Jiri Matas; Graph-Cut RANSAC, Conference on Computer Vision and Pattern Recognition, 2018. It is available at http://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Graph-Cut_RANSAC_CVPR_2018_paper.pdf
💎 "Marker-less Augmented Reality" with OpenCV and OpenGL.
This c++ implementation RANSAC algorithm finds the n best fitting circles out of the given points.
C++/CUDA solutions, slides, and resources for Udacity's Intro to Computer Vision (UD810) course.
Using Euclidiean Clustering and RANSAC to detect Objects in Lidar captured Point Clouds (PCDs)
Latent RANSAC implementation, based on USAC
Implement: Sobel; Canny; Harris; Hough line; Fit line; RANSAC.
line, circle and ellipse detection in 2d images.
The Random Cluster Model for Robust Geometric Fitting
Involves the OpenCV based C++ implementation to detect and track roads for almost realtime performance
Explore object recognition, segmentation, and how to process depth data from camera sensors to help a robot better understand and navigate its world.
CPU implementation of the Image stitching using FAST. For FPGA implementation visit tharaka27-SocStitcher.
MODS with external deep descriptors/detectors
An in-depth tutorial on the theory of panorama stitching
Circles detections in images, using Hough Transform and RANSAC
Code for our ECCV 2018 paper "Affine Correspondences between Central Cameras for Rapid Relative Pose Estimation"
RANSAC algorithm implementation for road plane detection and segmentation in the lidar point cloud.
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