no code implementations • 21 Mar 2024 • Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rares Ambrus, Sergey Zakharov
We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image.
no code implementations • 20 Feb 2024 • Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad, Robert Lee, Katherine Liu, Rares Ambrus, Koichi Nishiwaki
This paper addresses the challenging problem of category-level pose estimation.
1 code implementation • ICCV 2023 • Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus
NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference.
Ranked #1 on Generalizable Novel View Synthesis on NERDS 360
no code implementations • CVPR 2023 • Stephen Tian, Yancheng Cai, Hong-Xing Yu, Sergey Zakharov, Katherine Liu, Adrien Gaidon, Yunzhu Li, Jiajun Wu
Learned visual dynamics models have proven effective for robotic manipulation tasks.
1 code implementation • CVPR 2023 • Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Rares Andrei Ambrus, Jeannette Bohg, Abhinav Valada, Thomas Kollar
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation.
no code implementations • 12 Dec 2022 • Sergey Zakharov, Rares Ambrus, Katherine Liu, Adrien Gaidon
Compact and accurate representations of 3D shapes are central to many perception and robotics tasks.
no code implementations • 6 Nov 2020 • Yorai Shaoul, Katherine Liu, Kyel Ok, Nicholas Roy
We show that self-labelling challenging triplets--choosing positive examples separated by large temporal distances and negative examples close in the descriptor space--improves the quality of the learned descriptors for the multi-object tracking task.