Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Su, Yongzhi"'
Autor:
Lin, Yongliang, Su, Yongzhi, Inuganti, Sandeep, Di, Yan, Ajilforoushan, Naeem, Yang, Hanqing, Zhang, Yu, Rambach, Jason
Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typically establish one-to-one correspondences between image pixels and 3D obj
Externí odkaz:
http://arxiv.org/abs/2405.10557
Autor:
Lin, Yongliang, Su, Yongzhi, Nathan, Praveen, Inuganti, Sandeep, Di, Yan, Sundermeyer, Martin, Manhardt, Fabian, Stricker, Didier, Rambach, Jason, Zhang, Yu
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image. While many existing data-driven methods achieve impressive performance, they tend to be time-consuming due to their reliance on re
Externí odkaz:
http://arxiv.org/abs/2311.12588
Autor:
Di, Yan, Zhang, Chenyangguang, Zhang, Ruida, Manhardt, Fabian, Su, Yongzhi, Rambach, Jason, Stricker, Didier, Ji, Xiangyang, Tombari, Federico
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
Externí odkaz:
http://arxiv.org/abs/2308.06383
Autor:
Su, Yongzhi, Di, Yan, Manhardt, Fabian, Zhai, Guangyao, Rambach, Jason, Busam, Benjamin, Stricker, Didier, Tombari, Federico
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 explicitl
Externí odkaz:
http://arxiv.org/abs/2211.01142
Autor:
Su, Yongzhi, Saleh, Mahdi, Fetzer, Torben, Rambach, Jason, Navab, Nassir, Busam, Benjamin, Stricker, Didier, Tombari, Federico
Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the
Externí odkaz:
http://arxiv.org/abs/2203.09418
Utilizing 6DoF(Degrees of Freedom) pose information of an object and its components is critical for object state detection tasks. We present IKEA Object State Dataset, a new dataset that contains IKEA furniture 3D models, RGBD video of the assembly p
Externí odkaz:
http://arxiv.org/abs/2111.08614
Akademický článek
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Akademický článek
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Publikováno v:
Journal of Magnetics. 22:315-325