  3D cars are commonly used in self-driving systems, virtual/augmented reality,
and games. However, existing 3D car datasets are either synthetic or
low-quality, presenting a significant gap toward the high-quality real-world 3D
car datasets and limiting their applications in practical scenarios. In this
paper, we propose the first large-scale 3D real car dataset, termed 3DRealCar,
offering three distinctive features. (1) \textbf{High-Volume}: 2,500 cars are
meticulously scanned by 3D scanners, obtaining car images and point clouds with
real-world dimensions; (2) \textbf{High-Quality}: Each car is captured in an
average of 200 dense, high-resolution 360-degree RGB-D views, enabling
high-fidelity 3D reconstruction; (3) \textbf{High-Diversity}: The dataset
contains various cars from over 100 brands, collected under three distinct
lighting conditions, including reflective, standard, and dark. Additionally, we
offer detailed car parsing maps for each instance to promote research in car
parsing tasks. Moreover, we remove background point clouds and standardize the
car orientation to a unified axis for the reconstruction only on cars without
background and controllable rendering. We benchmark 3D reconstruction results
with state-of-the-art methods across each lighting condition in 3DRealCar.
Extensive experiments demonstrate that the standard lighting condition part of
3DRealCar can be used to produce a large number of high-quality 3D cars,
improving various 2D and 3D tasks related to cars. Notably, our dataset brings
insight into the fact that recent 3D reconstruction methods face challenges in
reconstructing high-quality 3D cars under reflective and dark lighting
conditions. \textcolor{red}{\href{https://xiaobiaodu.github.io/3drealcar/}{Our
dataset is available here.}}
