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Title:Not all views are equal: active neural radiance fields
Author(s):Bojja, Bharat
Advisor(s):Wang, Yuxiong
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):neural radiance fields
active learning
deep learning
computer vision
Abstract:By modelling complex scenes via a continuous volumetric scene function, neural radiance fields (NeRF) achieves state-of-the-art results in novel view synthesis. However, since NeRF does not take into account prior information about the object content in the scene, it requires many image views and significant training time to achieve high-quality view synthesis. To reduce the number of views required, some follow-up works build on the NeRF model to take into account depth and appearance information. In this paper, we show that NeRF does not necessarily require many image views to achieve quality view synthesis if there is a way to identify the views that are most informative. There is currently no method that actively identifies how informative each view is to the eventual scene reconstruction. This is especially important when the dataset is extremely large, and it would be computationally expensive to train the model on all the images. We propose a reinforcement learning framework using the REINFORCE algorithm to actively select which viewpoints yield in the best view synthesis. We also investigate the Monte-Carlo Tree Search method as a potentially promising approach. Our methods demonstrates that it is possible achieve a dramatic improvement in performance by actively selecting a limited number of views.
Issue Date:2021-12-09
Rights Information:Copyright 2021 Bharat Bojja
Date Available in IDEALS:2022-04-29
Date Deposited:2021-12

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