Zobrazeno 1 - 10
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pro vyhledávání: '"Di, Yan"'
Autor:
Zhang, Ruida, Huang, Ziqin, Wang, Gu, Zhang, Chenyangguang, Di, Yan, Zuo, Xingxing, Tang, Jiwen, Ji, Xiangyang
While RGBD-based methods for category-level object pose estimation hold promise, their reliance on depth data limits their applicability in diverse scenarios. In response, recent efforts have turned to RGB-based methods; however, they face significan
Externí odkaz:
http://arxiv.org/abs/2409.15727
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:
Zhai, Guangyao, Örnek, Evin Pınar, Chen, Dave Zhenyu, Liao, Ruotong, Di, Yan, Navab, Nassir, Tombari, Federico, Busam, Benjamin
We present EchoScene, an interactive and controllable generative model that generates 3D indoor scenes on scene graphs. EchoScene leverages a dual-branch diffusion model that dynamically adapts to scene graphs. Existing methods struggle to handle sce
Externí odkaz:
http://arxiv.org/abs/2405.00915
During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved, especially in non-textured regions such as walls, ceilings, and furniture surfaces. This degradation si
Externí odkaz:
http://arxiv.org/abs/2403.11324
Autor:
Zhang, Ruida, Zhang, Chenyangguang, Di, Yan, Manhardt, Fabian, Liu, Xingyu, Tombari, Federico, Ji, Xiangyang
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 matc
Externí odkaz:
http://arxiv.org/abs/2403.10099
Precisely perceiving the geometric and semantic properties of real-world 3D objects is crucial for the continued evolution of augmented reality and robotic applications. To this end, we present Foundation Model Embedded Gaussian Splatting (FMGS), whi
Externí odkaz:
http://arxiv.org/abs/2401.01970
Autor:
Fu, Bowen, Wang, Gu, Zhang, Chenyangguang, Di, Yan, Huang, Ziqin, Leng, Zhiying, Manhardt, Fabian, Ji, Xiangyang, Tombari, Federico
Reconstructing hand-held objects from a single RGB image is a challenging task in computer vision. In contrast to prior works that utilize deterministic modeling paradigms, we employ a point cloud denoising diffusion model to account for the probabil
Externí odkaz:
http://arxiv.org/abs/2311.14189
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:
Chen, Yamei, Di, Yan, Zhai, Guangyao, Manhardt, Fabian, Zhang, Chenyangguang, Zhang, Ruida, Tombari, Federico, Navab, Nassir, Busam, Benjamin
Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. Existing works utilizing mean shapes often fall short of capturing this var
Externí odkaz:
http://arxiv.org/abs/2311.11125
Autor:
Di, Yan, Zhang, Chenyangguang, Wang, Chaowei, Zhang, Ruida, Zhai, Guangyao, Li, Yanyan, Fu, Bowen, Ji, Xiangyang, Gao, Shan
In this paper, we present ShapeMatcher, a unified self-supervised learning framework for joint shape canonicalization, segmentation, retrieval and deformation. Given a partially-observed object in an arbitrary pose, we first canonicalize the object b
Externí odkaz:
http://arxiv.org/abs/2311.11106