CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle
  Components
Davide Di Nucci Alessandro Simoni Matteo Tomei Luca Ciuffreda Roberto Vezzani Rita Cucchiara
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.