Print Email Facebook Twitter Depth Light Field Training (DeLFT) Title Depth Light Field Training (DeLFT): NeRF as a rendering primitive Author Toader, Mihnea (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Eisemann, E. (mentor) Kellnhofer, P. (mentor) Weinmann, M. (mentor) van Gemert, J.C. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-07-05 Abstract Neural radiance fields (NeRF) based solutions for novel view synthesis can achieve state of the art results. Recent work proposes models that take less time to render, need less training data or take up less space. However, few papers explore the use of NeRFs in classic rendering scenarios such as rasterization, which could contribute to wider adoption. Our paper tackles the issue of shadow generation and proposes a deep residual MLP network with fast evaluation times, that generates view-dependent shadow maps. The network distills the knowledge of an existing NeRF model and achieves the speedup through the use of neural light fields, by only doing one network forward per ray. Subject NERFShadow CastingNeural NetworksBachelor thesis To reference this document use: http://resolver.tudelft.nl/uuid:8340b236-02c8-4709-ada5-1aa5038cc582 Part of collection Student theses Document type bachelor thesis Rights © 2023 Mihnea Toader Files PDF Final_Paper_Mihnea_Toader.pdf 851.03 KB Close viewer /islandora/object/uuid:8340b236-02c8-4709-ada5-1aa5038cc582/datastream/OBJ/view