结合2D分割基础模型与3D Gaussian Splatting (3DGS):SAGA将2D分割基础模型(如Segment Anything Model, SAM)与3DGS相结合。3DGS使用可训练的3D高斯来表示3D场景,并提出了一种高效的可微分栅格化算法用于渲染和训练。 训练3D高斯的特征:在训练阶段,SAGA为每个3D高斯附加一个低维特征。这些特征基于SAM自动提取的掩模进...
InstantStyleGaussian展示了出色的风格迁移性能，能够生成高质量的3D场景，这些场景与参考图像的艺术风格更加...
To address this issue, we propose Gaussian Grouping, which extends Gaussian Splatting to jointly reconstruct and segment anything in open-world 3D scenes. We augment each Gaussian with a compact Identity Encoding, allowing the Gaussians to be grouped according to their object instance or stuff ...
Compared to the implicit NeRF representation, we show that the discrete and grouped 3D Gaussians can reconstruct, segment and edit anything in 3D with high visual quality, fine granularity and efficiency. Based on Gaussian Grouping, we further propose a local Gaussian Editing scheme, which shows ...
Gaussian Splatting to jointly reconstruct and segment anything in open-world 3D scenes. We augment each Gaussian with a compact Identity Encoding, allowing the Gaussians to be grouped according to their object instance or stuff membership in the 3D scene. Instead of resorting to expensive 3D ...
"GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping." ArXiv (2024). [paper] [code] [2024.03] Soroush Seifi, Daniel Olmeda Reino, Fabien Despinoy, Rahaf Aljundi. "Annotation Free Semantic Segmentation with Vision Foundation Models." ArXiv (2024). [paper] [202...
Segment Anything in High Quality [NeurIPS 2023]. Contribute to SysCV/sam-hq development by creating an account on GitHub.
; An empty string can be denoted by simply not writing anything after the equal ; sign, or by using the None keyword: ; foo = ; sets foo to an empty string ; foo = None ; sets foo to an empty string ; foo = "None" ; sets foo to the string 'None' ; If you use constants ...
提出了SA3D框架:该框架是一个创新性的3D分割方法,结合了2D分割基础模型Segment Anything Model (SAM)与辐射场模型(如NeRF和3D Gaussian Splatting),无需重新设计或重新训练即可执行3D分割任务。SA3D通过2D分割模型的扩展,提供了一种高效的方式,将2D图像信息提升到3D场景中,实现准确的3D分割。
The Data Engine in the Segment Anything Model provides a continuous stream of data, thereby addressing the issue of scarce medical image data. This article attempts to use image-enhancement techniques to provide high-quality data to the model and, thus, improve its accuracy. Finally, the ...