CrashCar101: Procedural Generation for Damage Assessment
Jens Parslov Erik Riise Dim P. Papadopoulos
In this paper, we are interested in addressing the problem of damage
assessment for vehicles, such as cars. This task requires not only detecting
the location and the extent of the damage but also identifying the damaged
part. To train a computer vision system for the semantic part and damage
segmentation in images, we need to manually annotate images with costly pixel
annotations for both part categories and damage types. To overcome this need,
we propose to use synthetic data to train these models. Synthetic data can
provide samples with high variability, pixel-accurate annotations, and
arbitrarily large training sets without any human intervention. We propose a
procedural generation pipeline that damages 3D car models and we obtain
synthetic 2D images of damaged cars paired with pixel-accurate annotations for
part and damage categories. To validate our idea, we execute our pipeline and
render our CrashCar101 dataset. We run experiments on three real datasets for
the tasks of part and damage segmentation. For part segmentation, we show that
the segmentation models trained on a combination of real data and our synthetic
data outperform all models trained only on real data. For damage segmentation,
we show the sim2real transfer ability of CrashCar101.