Search Results for author: Yongzhi Su

Found 7 papers, 4 papers with code

Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation

no code implementations17 May 2024 Yongliang Lin, Yongzhi Su, Sandeep Inuganti, Yan Di, Naeem Ajilforoushan, Hanqing Yang, Yu Zhang, Jason Rambach

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.

Object Pose Estimation

U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds

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.

3D Shape Retrieval Retrieval

OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection

no code implementations2 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.

Monocular 3D Object Detection Object +1

TGA: Two-level Group Attention for Assembly State Detection

no code implementations12 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.

Object object-detection +2

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