本章讨论实现动态NeRF的两种主要方法:变形的 ray bending 和骨架驱动的 deformation。NR-NeRF利用ray bending实现非刚性变形,无需显式监督,通过神经网络学习渲染。Skeleton-NeRF基于传统骨架动画,利用隐含代码和混合权重场产生变形场,实现动态渲染。两种方法均扩展了NeRF网络以处理复杂渲染效果,如子弹时间效果,未来研究将结...
Note: We don't have the ability to review paper PubDate: Oct 2022 Teams: University at Buffalo, ETH Zurich, InnoPeak Technology, and the University of Tübingen Writers: Liangchen Song, Anpei Chen, Zhong Li, Zhang Chen, Lele Chen, Junsong Yuan, Yi Xu, Andreas Geiger PDF:NeRFPlayer: A ...
Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF) [31], which trains a deep network to...
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原始NeRF中的繁琐体渲染被高效的可微溅射所取代,它直接将3D高斯点投影到2D平面上。3D-GS不仅具有实时渲染速度,而且更明确地表示场景,更容易操作场景表示。 In this section, we simply review the representation and rendering process of 3D-GS [13] in Sec. 3.1 and the formula of dynamic NeRFs based on ...
provided by grants from the Foundation of Research, Science and Technology; the Ministry of Science and Innovation (NERF Grant no. UOOX0807); the Ministry of Business, Innovation and Employment (Smart Ideas Phase 1; No. UOOX1403), the Otago Medical Research Foundation and Otago Innovation Ltd...
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Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular video, with promising performance. However, the monocular setting is known to be an under-constrained problem, and so met...
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