top of page

News to help your R&D in artificial intelligence, machine learning, robotics, computer vision, smart hardware

As an Amazon Associate I earn

from qualifying purchases

Writer's picturemorrislee

Put partial 3D point clouds into standard orientation with self-supervised ConDor

Updated: Apr 25, 2022

Put partial 3D point clouds into standard orientation with self-supervised ConDor


ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes

arXiv paper abstract https://arxiv.org/abs/2201.07788



Progress in 3D object understanding has relied on manually canonicalized shape datasets that contain instances with consistent position and orientation (3D pose).


... ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.


... build on top of Tensor Field Networks (TFNs) ... method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose.


... network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds.


ConDor can also learn to consistently co-segment object parts without any supervision.


... approach outperforms existing methods while enabling new applications such as operation on depth images and annotation transfer.



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 #ComputerVision #3D #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning


39 views0 comments

Comments


ClickBank paid link

bottom of page