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Dynamic NeRF: A Review

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arxiv 2405.08609 v1 pith:254TVTTF submitted 2024-05-14 cs.CV

Jinwei Lin This is my paper · ORCID
classification cs.CV
keywords nerfdynamicdevelopmentreviewanalysisapplicationsdetailedfirst
verification ladder T0 review T1 audit T2 compute T3 formal
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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 analysis of main principle and development of Dynamic NeRF is from 2021 to 2023, including the most of the Dynamic NeRF projects. What is more, with colorful and novel special designed figures and table, We also made a detailed comparison and analysis of different features of various of Dynamic. Besides, we analyzed and discussed the key methods to implement a Dynamic NeRF. The volume of the reference papers is large. The statements and comparisons are multidimensional. With a reading of this review, the whole development history and most of the main design method or principles of Dynamic NeRF can be easy understood and gained.

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Cited by 3 Pith papers

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  1. AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting

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    A single-pass 3D-native network upgrades low-quality Gaussian Splatting assets with local geometric anchors, delivering SOTA fidelity on a new benchmark at up to 10^5× the speed of optimization pipelines.

  2. E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras

    cs.CV 2025-08 conditional novelty 6.0 of 10

    E-4DGS is a deformable 3D Gaussian Splatting method that reconstructs dynamic scenes directly from multi-view event camera streams, outperforming event-to-image baseline approaches.

  3. Camera Trajectory Generation: A Comprehensive Survey of Methods, Metrics, and Future Directions

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A review that organizes camera trajectory generation into representation levels, algorithm families, evaluation metrics, and datasets.

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