no code implementations • 23 Apr 2022 • Qiaojun Feng, Nikolay Atanasov
A local mesh is reconstructed using an initialization and refinement stage.
no code implementations • ICCV 2021 • Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov
It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps.
no code implementations • 11 Mar 2021 • Tianyu Zhao, Qiaojun Feng, Sai Jadhav, Nikolay Atanasov
This paper considers online object-level mapping using partial point-cloud observations obtained online in an unknown environment.
no code implementations • 8 Mar 2021 • Qiaojun Feng, Nikolay Atanasov
This paper focuses on pose registration of different object instances from the same category.
no code implementations • 8 Mar 2021 • Qiaojun Feng, Yue Meng, Mo Shan, Nikolay Atanasov
We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes.
1 code implementation • 6 Jan 2021 • Qiaojun Feng, Nikolay Atanasov
Each local mesh is initialized from sparse depth measurements.
4 code implementations • 29 Jul 2020 • Mo Shan, Vikas Dhiman, Qiaojun Feng, Jinzhao Li, Nikolay Atanasov
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical.
no code implementations • 23 Dec 2016 • Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features.