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Compositional neural scene graph studies have shown that radiance fields canbe 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. Inthis letter, we propose a pipeline for learning unconstrained images andbuilding a dataset from processed images. To meet the requirements of thesimulator, which demands that the vehicle maintain clarity when the perspectivechanges and that the contour remains sharp from the background to avoidartifacts when editing, we design a radiation field of the vehicle, a crucialpart of the urban scene foreground. Through experiments, we demonstrate thatour model achieves competitive performance compared to baselines. Using thedatasets built from in-the-wild images, our method gradually presents acontrollable appearance editing function. We will release the dataset and codeon https://lty2226262.github.io/car-studio/ to facilitate further research inthe field.

Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images