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TransNeRF - Improving Neural Radiance fields using transfer learning for efficient scene reconstruction

Paul, Navneet (2021) TransNeRF - Improving Neural Radiance fields using transfer learning for efficient scene reconstruction.

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Abstract:Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings including forward­facing capture of bounded and unbounded scenes. We present a trans­fer learning­based method for neural radiance fields to efficiently synthesize novel views of complex scenes using only a sequence of sample images from the UAVid dataset. We build on transferring the feature color weights of a multilayer perceptron from low resolution images to high resolution scenes by modelling the density and color of the scene as a function of 3D coordinates, latent appearance encodings and viewing directions. Qualitative quantitative metric evaluations are conducted between our proposed approach and other pre­existing NeRF and NeRF++ models, delivering a significant im­provement over the later in synthesizing novel scenes at high quality and high fps from very less input number of captured images.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Electrical Engineering MSc (60353)
Link to this item:https://purl.utwente.nl/essays/88717
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