TL;DR
This review paper summarizes the recent developments, key principles, and comparative analysis of Dynamic Neural Radiance Fields (NeRF) from 2021 to 2023, highlighting their potential for practical 3D modeling applications.
Contribution
It provides a comprehensive overview of Dynamic NeRF research, including development history, implementation principles, and detailed comparisons of various methods and features.
Findings
Dynamic NeRF has gained significant attention for practical 3D applications.
The review highlights key implementation methods and design principles.
Comparative analysis of different Dynamic NeRF models is provided.
Abstract
Neural Radiance Field(NeRF) is an novel implicit method to achieve the 3D reconstruction and representation with a high resolution. After the first research of NeRF is proposed, NeRF has gained a robust developing power and is booming in the 3D modeling, representation and reconstruction areas. However the first and most of the followed research projects based on NeRF is static, which are weak in the practical applications. Therefore, more researcher are interested and focused on the study of dynamic NeRF that is more feasible and useful in practical applications or situations. Compared with the static NeRF, implementing the Dynamic NeRF is more difficult and complex. But Dynamic is more potential in the future even is the basic of Editable NeRF. In this review, we made a detailed and abundant statement for the development and important implementation principles of Dynamci NeRF. The…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications