Enhancing Temporal Smoothing in Dynamic Neural Radiance Fields

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dc.contributor.advisorКузьменко, Дмитроuk_UA
dc.contributor.authorВербицька, Маріяuk_UA
dc.date.accessioned2025-09-04T06:38:43Z
dc.date.available2025-09-04T06:38:43Z
dc.date.issued2025
dc.description.abstractIn 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.en_US
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/36422
dc.language.isoen_USen_US
dc.statusfirst publisheden_US
dc.subjectneural radiance fieldsen_US
dc.subjectview synthesisen_US
dc.subjectdynamic scenesen_US
dc.subjectblender dataseten_US
dc.subjectfilteringen_US
dc.subject3D scene reconstructionen_US
dc.subjectbachelor`s thesisen_US
dc.titleEnhancing Temporal Smoothing in Dynamic Neural Radiance Fieldsen_US
dc.title.alternativeОптимізація часової згладженості в динамічних нейронних полях випромінюванняuk_UA
dc.typeOtheren_US
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