1 code implementation • 21 Nov 2023 • Yongliang Lin, Yongzhi Su, Praveen Nathan, Sandeep Inuganti, Yan Di, Martin Sundermeyer, Fabian Manhardt, Didier Stricker, Jason Rambach, Yu Zhang
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image.
1 code implementation • ICCV 2023 • Yan Di, Chenyangguang Zhang, Ruida Zhang, Fabian Manhardt, Yongzhi Su, Jason Rambach, Didier Stricker, Xiangyang Ji, Federico Tombari
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target.
no code implementations • 2 Nov 2022 • Yongzhi Su, Yan Di, Fabian Manhardt, Guangyao Zhai, Jason Rambach, Benjamin Busam, Didier Stricker, Federico Tombari
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box.
1 code implementation • CVPR 2022 • Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari
Dense methods also improved pose estimation in the presence of occlusion.
1 code implementation • 16 Nov 2021 • Yongzhi Su, Mingxin Liu, Jason Rambach, Antonia Pehrson, Anton Berg, Didier Stricker
Utilizing 6DoF(Degrees of Freedom) pose information of an object and its components is critical for object state detection tasks.
no code implementations • 12 Oct 2020 • Hangfan Liu, Yongzhi Su, Jason Rambach, Alain Pagani
Assembly state detection, i. e., object state detection, has a critical meaning in computer vision tasks, especially in AR assisted assembly.