论文标题
Stilleben：在机器人技术中进行深度学习的现实场景综合
Stillleben: Realistic Scene Synthesis for Deep Learning in Robotics
论文作者
论文摘要
培训数据是深度学习方法的关键要素，但是对于机器人技术经常遇到的专用领域很难获得。我们描述了一个综合管道，该管道能够生成用于混乱的场景感知任务（例如语义分割，对象检测以及对应关系或姿势估计）的训练数据。我们的方法使用物理模拟在物理上逼真的，密集的场景中安排对象。排列的场景是使用具有随机外观和材料参数的高质量栅格化渲染的。模拟了摄像机传感器引入的噪声和其他转换。我们的管道可以在培训深度神经网络期间在线运行，从而在终身学习和迭代渲染方法中产生应用。我们通过在挑战性的YCB-Video数据集上学习语义细分来证明可用性，而无需实际使用任何培训框架，在这种情况下，我们的方法可以实现与常规训练的模型相当的性能。此外，我们在现实世界中的重新制定系统中显示了成功的应用。
Training data is the key ingredient for deep learning approaches, but difficult to obtain for the specialized domains often encountered in robotics. We describe a synthesis pipeline capable of producing training data for cluttered scene perception tasks such as semantic segmentation, object detection, and correspondence or pose estimation. Our approach arranges object meshes in physically realistic, dense scenes using physics simulation. The arranged scenes are rendered using high-quality rasterization with randomized appearance and material parameters. Noise and other transformations introduced by the camera sensors are simulated. Our pipeline can be run online during training of a deep neural network, yielding applications in life-long learning and in iterative render-and-compare approaches. We demonstrate the usability by learning semantic segmentation on the challenging YCB-Video dataset without actually using any training frames, where our method achieves performance comparable to a conventionally trained model. Additionally, we show successful application in a real-world regrasping system.
