2 citations · 2 across the 3 of their papers we have counts for
5 papers
Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation
Yongliang Lin, Yongzhi Su, Sandeep Inuganti +5
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 typicall…
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Yongliang Lin, Yongzhi Su, Praveen Nathan +7
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 p…
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
Yan Di, Chenyangguang Zhang, Ruida Zhang +6
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 s…
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Yongzhi Su, Yan Di, Fabian Manhardt +5
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 me…
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer +5
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 replace…