Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Planche, Benjamin"'
Autor:
Lou, Ange, Planche, Benjamin, Gao, Zhongpai, Li, Yamin, Luan, Tianyu, Ding, Hao, Zheng, Meng, Chen, Terrence, Wu, Ziyan, Noble, Jack
Numerous recent approaches to modeling and re-rendering dynamic scenes leverage plane-based explicit representations, addressing slow training times associated with models like neural radiance fields (NeRF) and Gaussian splatting (GS). However, merel
Externí odkaz:
http://arxiv.org/abs/2410.14169
Autor:
Wang, Bin, Choudhuri, Anwesa, Zheng, Meng, Gao, Zhongpai, Planche, Benjamin, Deng, Andong, Liu, Qin, Chen, Terrence, Bagci, Ulas, Wu, Ziyan
Interactive segmentation aims to accurately segment target objects with minimal user interactions. However, current methods often fail to accurately separate target objects from the background, due to a limited understanding of order, the relative de
Externí odkaz:
http://arxiv.org/abs/2410.12214
Autor:
Peng, Qucheng, Planche, Benjamin, Gao, Zhongpai, Zheng, Meng, Choudhuri, Anwesa, Chen, Terrence, Chen, Chen, Wu, Ziyan
Recent advancements in 3D reconstruction methods and vision-language models have propelled the development of multi-modal 3D scene understanding, which has vital applications in robotics, autonomous driving, and virtual/augmented reality. However, cu
Externí odkaz:
http://arxiv.org/abs/2410.07577
Novel view synthesis has advanced significantly with the development of neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS). However, achieving high quality without compromising real-time rendering remains challenging, particularly for phy
Externí odkaz:
http://arxiv.org/abs/2410.04974
Conventional 3D medical image segmentation methods typically require learning heavy 3D networks (e.g., 3D-UNet), as well as large amounts of in-domain data with accurate pixel/voxel-level labels to avoid overfitting. These solutions are thus extremel
Externí odkaz:
http://arxiv.org/abs/2408.14427
Positioning patients for scanning and interventional procedures is a critical task that requires high precision and accuracy. The conventional workflow involves manually adjusting the patient support to align the center of the target body part with t
Externí odkaz:
http://arxiv.org/abs/2407.14903
Autor:
Luan, Tianyu, Gao, Zhongpai, Xie, Luyuan, Sharma, Abhishek, Ding, Hao, Planche, Benjamin, Zheng, Meng, Lou, Ange, Chen, Terrence, Yuan, Junsong, Wu, Ziyan
We introduce a novel bottom-up approach for human body mesh reconstruction, specifically designed to address the challenges posed by partial visibility and occlusion in input images. Traditional top-down methods, relying on whole-body parametric mode
Externí odkaz:
http://arxiv.org/abs/2407.09694
Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applications due to computational bottlenecks, especially for accurate but heavy
Externí odkaz:
http://arxiv.org/abs/2406.02518
For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures. While it can significantly improve multiplanar soft tissue imaging
Externí odkaz:
http://arxiv.org/abs/2403.05758
3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms. Existing CNN-based end-to-end patient modeling solutions typically require a) customized network designs demanding la
Externí odkaz:
http://arxiv.org/abs/2403.03217