Neural Radiance Fields (NeRFs) have gained widespread recognition as a highly effective technique for representing 3D reconstructions of objects and scenes derived from sets of images. Despite their efficiency, NeRF models can pose challenges in certain scenarios such as vehicle inspection, where the lack of sufficient data or the presence of challenging elements (e.g. reflections) strongly impact the accuracy of the reconstruction. To this aim, we introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques. The dataset is publicly released at https://aimagelab.ing.unimore.it/go/carpatch and can be used as an evaluation guide and as a baseline for future work on this challenging topic.
@article{arxiv.2307.12718,
title = {CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components},
author = {Davide Di Nucci and Alessandro Simoni and Matteo Tomei and Luca Ciuffreda and Roberto Vezzani and Rita Cucchiara},
journal= {arXiv preprint arXiv:2307.12718},
year = {2023}
}