论文标题
Sidod：3D对象的合成图像数据集，构成分散因子
SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors
论文作者
论文摘要
我们提出了一个新的，公共可用的图像数据集，该数据集由NVIDIA深度学习数据合成器生成，旨在用于对象检测，姿势估计和跟踪应用程序。该数据集包含144K立体图像对，将三个具有多达10个对象的三个影片虚拟环境的摄像机视点（从YCB数据集的21个对象模型随机选择[1]）和飞行干扰器。物体和相机姿势，场景照明以及对象和干扰物的数量是随机的。每个提供的视图包括RGB，深度，分割和表面正常图像，所有像素级别。我们描述了我们的域随机化方法，并提供了对产生数据集的决策的见解。
We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.
