Enhancing Temporal Smoothing in Dynamic Neural Radiance Fields
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Date
2025
Authors
Вербицька, Марія
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Abstract
In this work, we conduct an end-to-end training and fine-tuning process for the Neural Radiance Field (NeRF) model [1] and introduce 4 experimental cases with filtering techniques [2] designed to strengthen the rendering performance. We evaluate our modifications on synthetic image data of the articulated objects. For this project, we chose the architecture of the Knowledge NeRF model [3]. It includes an original PyTorch NeRF implementation [4] alongside a projection module for dynamic scenes extension. Incorporating the rendering step adjustments allows for better results without requiring complete model re-training. Our study covers the theoretical basis of the 3D scene reconstruction problem [5] alongside the NeRF architecture, such as radiance field, volume rendering, the concept of coarse and fine networks etc. [1], provides a trained and fine-tuned model for one object of a specified motion type, and suggests four methods to handle postprocessing in Knowledge NeRF better.
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Keywords
neural radiance fields, view synthesis, dynamic scenes, blender dataset, filtering, 3D scene reconstruction, bachelor`s thesis