A Tool for Building Multi-purpose and Multi-pose Synthetic Data Sets

Abstract

Modern computer vision methods typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to propose a novel approach of designing and generating large scale multi-purpose image data sets from 3D object models directly, captured from multiple categorized camera viewpoints and controlled environmental conditions. The set of rendered images provide data for geometric computer vision problems such as depth estimation, camera pose estimation, 3D box estimation, 3D reconstruction, camera calibration, and also pixel-perfect ground truth for scene understanding problems, such as: semantic and instance segmentation, object detection, just to cite a few. In this paper, we also survey the most well-known synthetic data sets used in computer vision tasks, pointing out the relevance of rendering images for training deep neural networks. When compared to similar tools, our generator contains a wide set of features easy to extend, besides allowing for building sets of images in the MSCOCO format, so ready for deep learning works. To the best of our knowledge, the proposed tool is the first one to generate large-scale, multi-pose, synthetic data sets automatically, allowing for training and evaluation of supervised methods for all of the covered features.