RelPose++: Recovering 6D Poses from Sparse-view Observations SimpleRecon3D Reconstruction without 3D Convolutions state-of-the-art depth accuracy competitive 3D scene reconstruction

 https://arxiv.org/pdf/2305.04926.pdf

 https://github.com/ZhongqunZHANG/awesome-6d-object

 https://openaccess.thecvf.com/content_cvpr_2016/papers/Doumanoglou_Recovering_6D_Object_CVPR_2016_paper.pdf

 RelPose++: Recovering 6D Poses from Sparse-view Observations  Amy Lin* Jason Y. Zhang* Deva Ramanan Shubham Tulsiani   Carnegie Mellon University

https://amyxlase.github.io/relpose-plus-plus/ 

https://arxiv-sanity-lite.com/

https://github.com/YoungXIAO13/ObjectPoseEstimationSummary/blob/master/paper.md

Awesome Object Pose Estimation Recovering 6D Poses Deep-6DPose Recovering 6D Object Pose

https://github.com/VITA-Group/NeRF-SOS

NeRF-SOS: Any-View Self-supervised Object Segmentation ...
https://arxiv.org › cs
 This paper carries out the exploration of self-supervised learning for object segmentation using NeRF for complex real-world scenes. Our  

SimpleRecon3D Reconstruction without 3D Convolutions state-of-the-art depth accuracy  competitive 3D scene reconstruction

SimpleRecon: 3D Reconstruction Without 3D Convolutions | SpringerLink

https://link.springer.com/chapter/10.1007/978-3-031-19827-4_1

[PDF] SimpleRecon: 3D Reconstruction Without 3D Convolutions

https://nianticlabs.github.io/simplerecon/

https://www.marktechpost.com/2022/09/16/simplerecon-a-computer-vision-framework-that-produces-3d-reconstructions-without-the-use-of-3d-convolutions/

https://arxiv.org/pdf/2208.14743.pdf

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136930001.pdf

https://github.com/rerun-io/simplerecon