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- W2998939343 abstract "The objective of this contribution is to introduce setforge, a set of software tools for synthetic data generation for convolutional neural network (CNN) training. Our focus is on CNNs for 6-degree-of-freedom pose estimation using RGB-D data. To determine the pose of physical objects in 6-degree-of-freedom is an essential task for many augmented reality applications. The recent years have shown the advent of trainable methods such as CNNs and others. However, those approaches require training data. The tools, this paper introduces, allow one to generate training data from 3D models. They come with plenty of features for random data generation and augmentation, adapting colors, hue, and noise. We contribute these tools as open-source software available on Github. A prototype CNN demonstrates how one can utilize it. An augmented reality demo application also shows its real-time pose estimation performance." @default.
- W2998939343 created "2020-01-23" @default.
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- W2998939343 date "2019-10-01" @default.
- W2998939343 modified "2023-09-23" @default.
- W2998939343 title "Setforge - Synthetic RGB-D Training Data Generation to Support CNN-Based Pose Estimation for Augmented Reality" @default.
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- W2998939343 doi "https://doi.org/10.1109/ismar-adjunct.2019.00-39" @default.
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