no code implementations • 5 Apr 2024 • Xingyu Liu, Chenyangguang Zhang, Gu Wang, Ruida Zhang, Xiangyang Ji
In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to-real domain gap.
1 code implementation • 15 Mar 2024 • Ruida Zhang, Chenyangguang Zhang, Yan Di, Fabian Manhardt, Xingyu Liu, Federico Tombari, Xiangyang Ji
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly match the target.
1 code implementation • 18 Nov 2023 • Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, Benjamin Busam
Leveraging the advantage of DINOv2 in providing SE(3)-consistent semantic features, we hierarchically extract two types of SE(3)-invariant geometric features to further encapsulate local-to-global object-specific information.
1 code implementation • 18 Nov 2023 • Yan Di, Chenyangguang Zhang, Chaowei Wang, Ruida Zhang, Guangyao Zhai, Yanyan Li, Bowen Fu, Xiangyang Ji, Shan Gao
In this paper, we present ShapeMatcher, a unified self-supervised learning framework for joint shape canonicalization, segmentation, retrieval and deformation.
no code implementations • 18 Oct 2023 • Chenyangguang Zhang, Guanlong Jiao, Yan Di, Gu Wang, Ziqin Huang, Ruida Zhang, Fabian Manhardt, Bowen Fu, Federico Tombari, Xiangyang Ji
Previous works concerning single-view hand-held object reconstruction typically rely on supervision from 3D ground-truth models, which are hard to collect in real world.
no code implementations • 15 Aug 2023 • Yan Di, Chenyangguang Zhang, Pengyuan Wang, Guangyao Zhai, Ruida Zhang, Fabian Manhardt, Benjamin Busam, Xiangyang Ji, Federico Tombari
However, such strategies fail to consistently align the denoised point cloud with the given image, leading to unstable conditioning and inferior performance.
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 • 13 Aug 2022 • Ruida Zhang, Yan Di, Fabian Manhardt, Federico Tombari, Xiangyang Ji
In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network.
1 code implementation • 30 Jul 2022 • Ruida Zhang, Yan Di, Zhiqiang Lou, Fabian Manhardt, Federico Tombari, Xiangyang Ji
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories.
3 code implementations • CVPR 2022 • Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xiangyang Ji, Nassir Navab, Federico Tombari
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.
Ranked #1 on 6D Pose Estimation on LineMOD (Mean ADD-S metric)