Car-Studio: Learning Car Radiance Fields from Single-View and Endless
  In-the-wild Images
Tianyu Liu Hao Zhao Yang Yu Guyue Zhou Ming Liu
Compositional neural scene graph studies have shown that radiance fields can
be an efficient tool in an editable autonomous driving simulator. However,
previous studies learned within a sequence of autonomous driving datasets,
resulting in unsatisfactory blurring when rotating the car in the simulator. In
this letter, we propose a pipeline for learning unconstrained images and
building a dataset from processed images. To meet the requirements of the
simulator, which demands that the vehicle maintain clarity when the perspective
changes and that the contour remains sharp from the background to avoid
artifacts when editing, we design a radiation field of the vehicle, a crucial
part of the urban scene foreground. Through experiments, we demonstrate that
our model achieves competitive performance compared to baselines. Using the
datasets built from in-the-wild images, our method gradually presents a
controllable appearance editing function. We will release the dataset and code
on https://lty2226262.github.io/car-studio/ to facilitate further research in
the field.