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Authors: Kohei Fukuda ; Takio Kurita and Hiroaki Aizawa

Affiliation: Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan

Keyword(s): Neural Radiance Fields, Novel View Synthesis.

Abstract: Neural Radiance Fields (NeRF) has impacted computer graphics and computer vision by enabling fine 3D representations using neural networks. However, depending on the data (especially on synthetic datasets with single-color backgrounds), the neural network training of NeRF is often unstable, and the rendering results become poor. This paper proposes a method to sample the informative pixels to remedy these shortcomings. The sampling method consists of two phases. In the early stage of learning (up to 1/10 of all iterations), the sampling probability is determined based on the edge strength obtained by edge detection. Also, we use the squared errors between the ground truth and the estimated color of the pixels for sampling. The introduction of these tweaks improves the learning of NeRF. In the experiment, we confirmed the effectiveness of the method. In particular, for small amounts of data, the training process of the neural network for NeRF was accelerated and stabilized.

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Paper citation in several formats:
Fukuda, K.; Kurita, T. and Aizawa, H. (2024). Important Pixels Sampling for NeRF Training Based on Edge Values and Squared Errors Between the Ground Truth and the Estimated Colors. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 102-111. DOI: 10.5220/0012346200003660

@conference{visapp24,
author={Kohei Fukuda. and Takio Kurita. and Hiroaki Aizawa.},
title={Important Pixels Sampling for NeRF Training Based on Edge Values and Squared Errors Between the Ground Truth and the Estimated Colors},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={102-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012346200003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Important Pixels Sampling for NeRF Training Based on Edge Values and Squared Errors Between the Ground Truth and the Estimated Colors
SN - 978-989-758-679-8
IS - 2184-4321
AU - Fukuda, K.
AU - Kurita, T.
AU - Aizawa, H.
PY - 2024
SP - 102
EP - 111
DO - 10.5220/0012346200003660
PB - SciTePress

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