ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
- URL: http://arxiv.org/abs/2211.13762v1
- Date: Thu, 24 Nov 2022 19:00:02 GMT
- Title: ScanNeRF: a Scalable Benchmark for Neural Radiance Fields
- Authors: Luca De Luigi, Damiano Bolognini, Federico Domeniconi, Daniele De
Gregorio, Matteo Poggi, Luigi Di Stefano
- Abstract summary: ScanNeRF is a dataset characterized by several train/val/test splits aimed at benchmarking the performance of modern NeRF methods under different conditions.
We evaluate three cutting-edge NeRF variants on it to highlight their strengths and weaknesses.
The dataset is available on our project page, together with an online benchmark to foster the development of better and better NeRFs.
- Score: 21.973450071630676
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose the first-ever real benchmark thought for
evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering
(NR) frameworks. We design and implement an effective pipeline for scanning
real objects in quantity and effortlessly. Our scan station is built with less
than 500$ hardware budget and can collect roughly 4000 images of a scanned
object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset
characterized by several train/val/test splits aimed at benchmarking the
performance of modern NeRF methods under different conditions. Accordingly, we
evaluate three cutting-edge NeRF variants on it to highlight their strengths
and weaknesses. The dataset is available on our project page, together with an
online benchmark to foster the development of better and better NeRFs.
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