  In recent years, Neural Radiance Fields (NeRF) have demonstrated significant
advantages in representing and synthesizing 3D scenes. Explicit NeRF models
facilitate the practical NeRF applications with faster rendering speed, and
also attract considerable attention in NeRF compression due to its huge storage
cost. To address the challenge of the NeRF compression study, in this paper, we
construct a new dataset, called Explicit-NeRF-QA. We use 22 3D objects with
diverse geometries, textures, and material complexities to train four typical
explicit NeRF models across five parameter levels. Lossy compression is
introduced during the model generation, pivoting the selection of key
parameters such as hash table size for InstantNGP and voxel grid resolution for
Plenoxels. By rendering NeRF samples to processed video sequences (PVS), a
large scale subjective experiment with lab environment is conducted to collect
subjective scores from 21 viewers. The diversity of content, accuracy of mean
opinion scores (MOS), and characteristics of NeRF distortion are
comprehensively presented, establishing the heterogeneity of the proposed
dataset. The state-of-the-art objective metrics are tested in the new dataset.
Best Person correlation, which is around 0.85, is collected from the
full-reference objective metric. All tested no-reference metrics report very
poor results with 0.4 to 0.6 correlations, demonstrating the need for further
development of more robust no-reference metrics. The dataset, including NeRF
samples, source 3D objects, multiview images for NeRF generation, PVSs, MOS, is
made publicly available at the following location:
https://github.com/LittlericeChloe/Explicit_NeRF_QA.
