Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset

arXiv:2404.08778v1 Announce Type: cross
Abstract: This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for …

abstract arxiv classification cs.cv cs.lg data dataset domain gap images industrial networks neural networks paper serve sim synthetic synthetic data training type world