@inproceedings{30a0a2fc6d8e46b8a978ae3db371fc45,
title = "Two is Better than One: Achieving High-Quality 3D Scene Modeling with a NeRF Ensemble",
abstract = "Neural Radiance Field (NeRF) is a popular method for synthesizing novel views of a scene from a set of input images. While NeRF has demonstrated state-of-the-art performance in several applications, it suffers from high computational requirements. Recent works have attempted to address these issues by including explicit volumetric information, which makes the optimization process difficult when fine-graining the voxel grids. In this paper, we propose an ensemble approach that combines the strengths of two NeRF models to achieve superior results compared to state-of-the-art architectures, with a similar number of parameters. Experimental results show that our ensemble approach is a promising strategy for performance enhancement, and beats vanilla approaches under the same parameter{\textquoteright}s cardinality constraint.",
keywords = "3D scene modeling, Compression, Ensemble, NeRF",
author = "\{Di Sario\}, Francesco and Riccardo Renzulli and Enzo Tartaglione and Marco Grangetto",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; Proceedings of the 22nd International Conference on Image Analysis and Processing, ICIAP 2023 ; Conference date: 11-09-2023 Through 15-09-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1007/978-3-031-43153-1\_27",
language = "English",
isbn = "9783031431524",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "320--331",
editor = "Foresti, \{Gian Luca\} and Andrea Fusiello and Edwin Hancock",
booktitle = "Image Analysis and Processing – ICIAP 2023 - 22nd International Conference, ICIAP 2023, Proceedings",
}