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Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation.
Available from: https://arxiv.org/pdf/1903.03953
Ren X, Luo J, Solowjow E, Ojea JA, Gupta A, Tamar A, et al.: Domain Randomization for Active Pose Estimation · 1903
Earlier work this paper cites.
Available from: https://arxiv.org/pdf/1907.07061
Nowruzi FE, Kapoor P, Kolhatkar D, Hassanat FA, Laganiere R, Rebut J.: How much real data do we actually need: Analyzing object detection performance using synthetic and real data · 1907
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Available from: https://arxiv.org/pdf/1911.01911
Denninger M, Sundermeyer M, Winkelbauer D, Zidan Y, Olefir D, Elbadrawy M, et al.: BlenderProc · 1911
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Synthetic to Real Adaptation with Generative Correlation Alignment Networks
X Peng, K Saenko · 1991
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The Pascal Visual Object Classes (VOC) Challenge
Everingham M, van Gool L, Williams CKI, Winn J, Zisserman A · 2010
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Generative Adversarial Nets
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al · 2014
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Domain randomization for transferring deep neural networks from simulation to the real world
Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P · 2017
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Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
D Dwibedi, I Misra, M Hebert · 2017
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Learning from Simulated and Unsupervised Images through Adversarial Training
A Shrivastava, T Pfister, O Tuzel, J Susskind, W Wang, R Webb · 2017
Earlier work this paper cites.
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
S Bell, C L Zitnick, K Bala, R Girshick · 2017
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Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation
J Tremblay, T To, S Birchfield · 2018
Cited alongside, same era.
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
S Sankaranarayanan, Y Balaji, A Jain, S N Lim, R Chellappa · 2018
Cited alongside, same era.
Training CNNs from Synthetic Data for Part Handling in Industrial Environments
M Andulkar, J Hodapp, T Reichling, M Reichenbach, U Berger · 2018
Cited alongside, same era.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
A Prakash, S Boochoon, M Brophy, D Acuna, E Cameracci, G State, et al · 2019
Cited alongside, same era.
An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Detection
S Hinterstoisser, O Pauly, H Heibel, M Martina, M Bokeloh · 2019
Cited alongside, same era.
Towards Fully-Synthetic Training for Industrial Applications
Mayershofer C, Ge T, Fottner J · 2021
Later among the works it cites.
Generating Images with Physics-Based Rendering for an Industrial Object Detection Task: Realism versus Domain Randomization
Eversberg L, Lambrecht J · 2021
Later among the works it cites.
A Review of Physics Simulators for Robotic Applications
Collins J, Chand S, Vanderkop A, Howard D · 2021
Later among the works it cites.
A Generation Method of Synthetic Images with Reduced Domain Gap for Car Detection
Y Huangfu, W Deng, B Ren, J Ding · 2021
Later among the works it cites.
Object Re-Identification with Synthetic Training Data in Industrial Environments
J Dümmel, X Gao · 2021
Later among the works it cites.
Generating Synthetic Training Data for Assembly Processes
Dümmel J, Kostik V, Oellerich J · 2021
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T Hodaň, V Vineet, R Gal, E Shalev, J Hanzelka, T Connell, et al · 2019
Cited alongside, same era.
BlenderProc: Reducing the Reality Gap with Photorealistic Rendering
Denninger M, Sundermeyer M, Winkelbauer D, Olefir D, Hodan T, Zidan Y, et al · 2020
Cited alongside, same era.
Style-transfer GANs for bridging the domain gap in synthetic pose estimator training
P Rojtberg, T Pöllabauer, A Kuijper · 2020
Cited alongside, same era.
Test Method for Measuring the Simulation-to-Reality Gap of Camera-based Object Detection Algorithms for Autonomous Driving
F Reway, A Hoffmann, D Wachtel, W Huber, A Knoll, E Ribeiro · 2020
Cited alongside, same era.
Available from: https://arxiv.org/pdf/1807.09834
Borrego J, Dehban A, Figueiredo R, Moreno P, Bernardino A, Santos-Victor J.: Applying Domain Randomization to Synthetic Data for Object Category Detection
Cited in the paper.
Available from: https://arxiv.org/pdf/1804.06516
Tremblay J, Prakash A, Acuna D, Brophy M, Jampani V, Anil C, et al.: Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Cited in the paper.
Available from: https://arxiv.org/pdf/1702.07836
Georgakis G, Mousavian A, Berg AC, Kosecka J.: Synthesizing Training Data for Object Detection in Indoor Scenes
Cited in the paper.
Later among the works it cites.
Synthetic Object Recognition Dataset for Industries
C A Akar, J Tekli, D Jess, M Khoury, M Kamradt, M Guthe · 2022
Later among the works it cites.
Towards Synthetic AI Training Data for Image Classification in Intralogistic Settings
Schoepflin D, Iyer K, Gomse M, Schüppstuhl T · 2022
Later among the works it cites.
CAD2Render: A Modular Toolkit for GPU-Accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry; 2023. p. 583–592
Moonen S, Vanherle B, de Hoog J, Bourgana T, Bey-Temsamani A, Michiels N · 2023
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