Seeing Beyond Appearance  Mapping Real Images into Geometrical Domains for Unsupervised CADbased Recognition While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domainrelevant data needed for their training. Therefore, it has become common practice to use available synthetic samples along domain adaptation schemes to prepare algorithms for the target domain. Tackling this problem from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train taskspecific methods. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly improves their performance. As this mapping is easier to learn than the opposite one (i.e., to generate realistic features to augment the source samples), we demonstrate how our whole solution can be trained purely on augmented synthetic data and still performs better than methods trained with domainrelevant information (e.g., real images or realistic textures for the 3D models). Applying our approach to object recognition from textureless CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and to achieve a more refined mapping for unseen color images.

The Impact of Domain Randomization on Object Detection: A Case Study on Parametric Shapes and Synthetic Textures * Recent advances in deep learningxe2x80x93based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pretrained on standard datasets and finetune them for specific application domains with a small set of domainxe2x80x93relevant images. In this work, we show that using synthetic datasets that are not necessarily photorealistic can be a better alternative to simply finetune pretrained networks. Specifically, our results show an impressive 25%improvement in the mAP metric over a finetuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.