An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection

Abstract

Deep learning methods typically require vast amounts of training data to reach their full potential. While some publicly available datasets exists, domain specific data always needs to be collected and manually labeled, an expensive, time consuming and error prone process. Training with synthetic data is therefore very lucrative, as dataset creation and labeling comes for free. We propose a novel method for creating purely synthetic training data for object detection. We leverage a large dataset of 3D background models and densely render them using full domain randomization. This yields background images with realistic shapes and texture on top of which we render the objects of interest. During training, the data generation process follows a curriculum strategy guaranteeing that all foreground models are presented to the network equally under all possible poses and conditions with increasing complexity. As a result, we entirely control the underlying statistics and we create optimal training samples at every stage of training. Using a set of 64 retail objects, we demonstrate that our simple approach enables the training of detectors that outperform models trained with real data on a challenging evaluation dataset.

References in corpus (9)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
• Learning from Synthetic Humans
• Domain Separation Networks
• Beyond Sharing Weights for Deep Domain Adaptation
• Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
• Object Detection Using Deep CNNs Trained on Synthetic Images
• Applying Domain Randomization to Synthetic Data for Object Category Detection
• Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection

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• NViSII: A Scriptable Tool for Photorealistic Image Generation
• Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings
• SynthText3D: Synthesizing Scene Text Images from 3D Virtual Worlds
• A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
• Efficient Realistic Data Generation Framework leveraging Deep Learning-based Human Digitization