170 citations · 332 across the 10 of their papers we have counts for
10 papers
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
Kaifeng Zhang, Yang Fu, Shubhankar Borse +3
While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even…
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose
Yang Fu, Ishan Misra, Xiaolong Wang
We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of…
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset
Yang Fu, Xiaolong Wang
6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation…
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu +4
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipula…
Learning to Track Instances without Video Annotations
Yang Fu, Sifei Liu, Umar Iqbal +3
Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation…
Clique: Spatiotemporal Object Re-identification at the City Scale
Tiantu Xu, Kaiwen Shen, Yang Fu +2
Object re-identification (ReID) is a key application of city-scale cameras. While classic ReID tasks are often considered as image retrieval, we treat them as spatiotemporal querie…