Match 3D points with 99% outliers quickly with VOCRA
Match 3D points with 99% outliers quickly with VOCRA
Practical, Fast and Robust Point Cloud Registration for 3D Scene Stitching and Object Localization
arXiv paper abstract https://arxiv.org/abs/2111.04228v1
arXiv PDF paper https://arxiv.org/pdf/2111.04228v1.pdf
3D point cloud registration ranks among the most fundamental problems in remote sensing, photogrammetry, robotics and geometric computer vision.
... existing robust solvers may encounter high computational cost or restricted robustness, ... propose ... VOCRA (VOting with Cost function and Rotating Averaging), for the point cloud registration problem with extreme outlier rates.
... first ... employ the Tukey's Biweight robust cost ... effective in distinguishing true inliers from outliers even with extreme (99%) outlier rates.
... second ... designing a time-efficient consensus maximization paradigm based on robust rotation averaging, serving to seek inlier candidates among the correspondences.
Finally, ... apply Graduated Non-Convexity with Tukey's Biweight (GNC-TB) to estimate the correct transformation
... show that our solver VOCRA is robust against over 99% outliers and more time-efficient than the state-of-the-art competitors.
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Comments