This talk will explain the basics and, also, the state-of-the-art of robust model estimation in computer vision. Robust model fitting problems appear in most of the vision applications involving real-world data. In such cases, the data consists of noisy points (inliers) originating from a single of multiple geometric models, and likely contain a large amount of large-scale measurement errors, i.e., outliers. The objective is to find the unknown models (e.g., 6D motion of objects or cameras) interpreting the scene.The sub-topics discussed are as follows:- Basics of robust model fitting in computer vision.- Exploiting spatial data to improve the accuracy and speed up the procedure.- Ways to avoid setting the noise scale (via the inlier-outlier threshold) manually.The talk is based on CVPR the papers by the speaker :Graph-Cut RANSAChttps://arxiv.org/abs/1706.00984MAGSAC: marginalizing sample consensushttps://arxiv.org/abs/1803.07469MAGSAC++, a fast, reliable and accurate robust estimatorhttps://arxiv.org/abs/1912.05909Code available are available at (C++ with Python binding)- https://github.com/danini/graph-cut-r...- https://github.com/danini/magsacTalk is based on CVPR 2020 tutorial "RANSAC in 2020" - the speaker is one of the organizers.Link: http://cmp.felk.cvut.cz/cvpr2020-rans...Lecture references: https://www.reddit.com/r/2D3DAI/comme...00:00 Intro - Taxonomy of Geometric Estimation Problems02:30 Single/Multi-Class S/M-Instance Fitting Applications04:45 Line Fitting with Outliers08:10 The SCSI Model Fitting Problem - Robust Loss11:36 Main Topics of the Talk13:08 Robust estimators16:46 Example Sources of Model Inaccuracy19:56 MINPRAN: A new robust estimator for computer vision23:10 Random Sample Aggregated Consensus (RANSAAC)26:20 Averaging Models30:44 The MAGSAC Approach39:57 The MAGSAC++ Approach41:46 Conclusions of this Section42:39 Exploiting the Spatial Coherence of Geometric Data46:47 Graph-Cut RANSAC49:10 RANSAC as a Labeling Problem52:58 Spatial Coherence in RANSAC (Potts model)58:17 NAPSAC Sampler01:01:42 Progressive NAPSAC01:03:56 Conclusion of this Section01:05:21 The Phototourism Dataset01:06:34 Evaluated methods01:13:53 Datasets: Hpatches-Sequences Viewpoints & EVD are 01:15:08 Are they really insensitive the threshold?01:17:40 Take home message and Discussion[Chapters were auto-generated using our proprietary software - contact us if you are interested in access to the software]Presenter BIO:Daniel Barath recently completed his PhD in computer science at the Eötvös Loránd University in Budapest, where he was advised by Dr. Levente Hajder. He was also working n the last 3 years at the Visual Recognition Group, Czech Technical University in Prague with Prof. Jiri Matas. He started his new position at ETH Zürich with Prof. Marc Pollefeys in 2021. His main research interest is in robust model fitting and minimal problems using non-traditional input data. Recently, he co-organized the RANSAC in 2020 full-day tutorial at CVPR 2020. Daniel serves as a reviewer for CVPR, ICCV, and ECCV, among others and has more than 10 papers published at CVPR/ICCV/ECCV/ICRA.-------------------------Find us at:Newsletter for updates about more events ➜ http://eepurl.com/gJ1t-DSub-reddit for discussions ➜ https://www.reddit.com/r/2D3DAI/Discord server for, well, discord ➜ https://discord.gg/MZuWSjFBlog ➜ https://2d3d.ai
This talk will explain the basics and, also, the state-of-the-art of robust model estimation in computer vision. Robust model fitting problems appear in most of the vision applications involving real-world data. In such cases, the data consists of noisy points (inliers) originating from a single of multiple geometric models, and likely contain a large amount of large-scale measurement errors, i.e., outliers. The objective is to find the unknown models (e.g., 6D motion of objects or cameras) interpreting the scene.The sub-topics discussed are as follows:- Basics of robust model fitting in computer vision.- Exploiting spatial data to improve the accuracy and speed up the procedure.- Ways to avoid setting the noise scale (via the inlier-outlier threshold) manually.The talk is based on CVPR the papers by the speaker :Graph-Cut RANSAChttps://arxiv.org/abs/1706.00984MAGSAC: marginalizing sample consensushttps://arxiv.org/abs/1803.07469MAGSAC++, a fast, reliable and accurate robust estimatorhttps://arxiv.org/abs/1912.05909Code available are available at (C++ with Python binding)- https://github.com/danini/graph-cut-r...- https://github.com/danini/magsacTalk is based on CVPR 2020 tutorial "RANSAC in 2020" - the speaker is one of the organizers.Link: http://cmp.felk.cvut.cz/cvpr2020-rans...Lecture references: https://www.reddit.com/r/2D3DAI/comme...00:00 Intro - Taxonomy of Geometric Estimation Problems02:30 Single/Multi-Class S/M-Instance Fitting Applications04:45 Line Fitting with Outliers08:10 The SCSI Model Fitting Problem - Robust Loss11:36 Main Topics of the Talk13:08 Robust estimators16:46 Example Sources of Model Inaccuracy19:56 MINPRAN: A new robust estimator for computer vision23:10 Random Sample Aggregated Consensus (RANSAAC)26:20 Averaging Models30:44 The MAGSAC Approach39:57 The MAGSAC++ Approach41:46 Conclusions of this Section42:39 Exploiting the Spatial Coherence of Geometric Data46:47 Graph-Cut RANSAC49:10 RANSAC as a Labeling Problem52:58 Spatial Coherence in RANSAC (Potts model)58:17 NAPSAC Sampler01:01:42 Progressive NAPSAC01:03:56 Conclusion of this Section01:05:21 The Phototourism Dataset01:06:34 Evaluated methods01:13:53 Datasets: Hpatches-Sequences Viewpoints & EVD are 01:15:08 Are they really insensitive the threshold?01:17:40 Take home message and Discussion[Chapters were auto-generated using our proprietary software - contact us if you are interested in access to the software]Presenter BIO:Daniel Barath recently completed his PhD in computer science at the Eötvös Loránd University in Budapest, where he was advised by Dr. Levente Hajder. He was also working n the last 3 years at the Visual Recognition Group, Czech Technical University in Prague with Prof. Jiri Matas. He started his new position at ETH Zürich with Prof. Marc Pollefeys in 2021. His main research interest is in robust model fitting and minimal problems using non-traditional input data. Recently, he co-organized the RANSAC in 2020 full-day tutorial at CVPR 2020. Daniel serves as a reviewer for CVPR, ICCV, and ECCV, among others and has more than 10 papers published at CVPR/ICCV/ECCV/ICRA.-------------------------Find us at:Newsletter for updates about more events ➜ http://eepurl.com/gJ1t-DSub-reddit for discussions ➜ https://www.reddit.com/r/2D3DAI/Discord server for, well, discord ➜ https://discord.gg/MZuWSjFBlog ➜ https://2d3d.ai
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
.jpg)
.jpg)