Ransac Tutorial 2020 Data

Overview

The RANSAC 2020 tutorial starter pack is a collection of data and resources for training and validating computer vision algorithms. It includes data for epipolar geometry training and validation, PnP methods, hyperparameter tuning for homography, and 3D point cloud stitching. The data sets are obtained from various sources and are available for download. Additionally, there are Jupyter notebooks and code snippets provided to demonstrate the format of the data and provide examples of evaluation and submission.

Features

• Epipolar geometry training and validation data, including 12 scenes with 100k image pairs each.
• PyTorch data loader for handling hdf5 files.
• PnP methods training and validation data from the EPOS dataset.
• Data for hyperparameter tuning for homography, including test and validation data.
• Jupyter notebooks showcasing the data format and providing toy evaluation examples.
• Code for running OpenCV RANSACs evaluation.
• 3D point cloud stitching data from the ETHZ Photogrammetry and Remote Sensing Group.

Summary

The RANSAC 2020 tutorial starter pack is a comprehensive collection of data sets and resources for training and validating computer vision algorithms. It covers various areas such as epipolar geometry, PnP methods, homography, and 3D point cloud stitching. The provided data sets can be downloaded and used for training and evaluating algorithms. Additionally, the included Jupyter notebooks and code snippets serve as guides for understanding the data format and performing evaluations. Overall, the RANSAC 2020 tutorial starter pack is a valuable resource for researchers and developers in the field of computer vision.