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We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images.
OpenGL programming guide: the official guide to learning OpenGL, versions 3.0 and 3.1
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“How useful is photo-realistic rendering for visual learning?,”
Movshovitz-Attias, et al., · 2016
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“The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes,”
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“Virtual worlds as proxy for multi-object tracking analysis,”
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Handa, et al., · 2016
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“Learning an appearance-based gaze estimator from one million synthesised images,”
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Huang, et al., · 2017
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Rozantsev, et al., · 2018
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Yu, et al., · 2016
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“A dataset for improved RGBD-based object detection and pose estimation for warehouse pick-and-place,”
Rennie, et al., · 2016
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“Faster R-CNN: towards real-time object detection with region proposal networks,”
Ren, et al., · 2017
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Csurka, · 2017
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“Physically-based rendering for indoor scene understanding using convolutional neural networks,”
Zhang, et al., · 2017
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Dwibedi, et al., · 2017
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Georgiev, et al., · 2018
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Hinterstoisser, et al., · 2018
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“Modeling visual context is key to augmenting object detection datasets,”
Dvornik, et al., · 2018
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“Deep object pose estimation for semantic robotic grasping of household objects,”
Tremblay, et al., · 2018
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Tremblay, et al., · 2018
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“Implicit 3D orientation learning for 6D object detection from RGB images,”
Sundermeyer, et al., · 2018
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Hodan, et al., · 2018
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Georgiev, et al., · 2018
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“BOP: Benchmark for 6D object pose estimation,”
Hodan, et al., · 2018
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