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5 papers
SRGS: Super-Resolution 3D Gaussian Splatting
Xiang Feng, Yongbo He, Linxi Chen +8
Low-resolution (LR) multi-view capture limits the fidelity of 3D Gaussian Splatting (3DGS). 3DGS super-resolution (SR) is therefore important, yet challenging because it must recov…
IE-SRGS: An Internal-External Knowledge Fusion Framework for High-Fidelity 3D Gaussian Splatting Super-Resolution
Xiang Feng, Tieshi Zhong, Shuo Chang +7
Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. E…
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Xiang Feng, Chengkai Wang, Chengyu Wu +4
Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning. In this paper, we propose FDNet, a Feature Decoupled Segmentati…
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
Yaqi Wang, Yifan Zhang, Xiaodiao Chen +24
Computer-aided design (CAD) tools are increasingly popular in modern dental practice, particularly for treatment planning or comprehensive prognosis evaluation. In particular, the…
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
Yunxiang Li, Bowen Jing, Zihan Li +2
Automatic segmentation of medical images is crucial in modern clinical workflows. The Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation without sp…