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.

翻译：神经辐射场（NeRF）作为一种基于图像集合对物体和场景进行三维重建的高效表示技术已获得广泛认可。尽管NeRF模型具备高效性，但在车辆检测等特定场景中仍面临挑战——数据不足或反射等困难元素的存在会严重影响重建精度。针对此问题，我们提出CarPatch——一个新型车辆合成基准数据集。除提供标注了内参和外参的图像集外，还为每个视角生成了对应的深度图和语义分割掩膜。本研究定义了全局与部件级评估指标，用于评价、比较并深入刻画若干前沿技术的性能特征。该数据集已在https://aimagelab.ing.unimore.it/go/carpatch公开发布，可作为该挑战性课题的评估指南与未来工作的基准参考。