Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

By Apostolia Tsirikoglou, Joel Kronander, Magnus Wrenninge, Jonas Unger. 2017

85 citations recorded by the source corpus.

We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The m ...

Subfields: Automotive Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Automotive Engineering

In Computer Graphics and Computer-Aided Design

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Citations in the corpus

Driving Policy Transfer via Modularity and Abstraction (2018, methodology): “ld has been studied extensively in computer vision and robotics. Synthetic data has been used for training and evaluation of perception systems in indoor environments [54,18,30] and driving scenarios [40,15, 29,38,45,22,47,2]. Direct transfer from simulation to reality remains difﬁcult even given hi”
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data (2018, background): “Applications like optical flow [1], [3], scene flow [4], classification [18], stereo [5], [6], semantic segmentation [8], [9], [19], 3D keypoint extraction [20], object pose [11] and 3D reconstruction [2], [7] have all benefited from the use of synthetic training data.”
Auxiliary Tasks in Multi-task Learning (2018, methodology): “Following publications proposed methods for the fully-automatic generation of training data from video games [15, 25] and custom simulators based on game engines [23] or procedural models [32].”
Geometric Image Synthesis (2018, background): “…of three parts, (i) the geometry parameters G which include the poses and shapes of objects, (ii) the appearance parameters A which describe the objects’ materials, textures and transparency and ﬁnally (iii) the environment parameters E which describe global conditions of the scene that aﬀect…”
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization (2018, background): “Several such simulated datasets have been created in recent years [1, 9, 4, 18, 24, 36, 19, 27, 26, 7, 21, 33].”
Synthetic Data for Deep Learning (2019, background): “They conclude that fine-tuning models trained on synthetic datasets with a small amount of real data is preferable to mixed training on a hybrid dataset with the same amount of real data, and that photorealism appears to be less important than the diversity of synthetic data; this runs contrary to t”
Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects (2018, background): “Given the deep learning hunger for large amounts of labeled training data, a recent research trend has focused on providing synthetic datasets for training [38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 20, 48].”