论文标题
使用高分散注意力逼真的合成数据检测和分割自定义对象
Detection and Segmentation of Custom Objects using High Distraction Photorealistic Synthetic Data
论文作者
论文摘要
我们展示了使用合成数据进行实例分割的直接且有用的方法。我们将此方法应用于基本案例，并通过定量分析得出见解。我们创建了一个新的公共数据集：旨在检测和分割任务的Expo标记数据集。该数据集包含5,000个合成的影像图像，其相应的像素完美分割地面真相。目的是在自定义对象的手动收集和注释的现实数据上实现高性能。我们通过创建目标对象的3D模型和其他可能的干扰对象来做到这一点，然后将它们放置在模拟环境中。为此任务选择了世博标记，这符合我们对自定义对象的要求，这是由于确切的纹理，大小和3D形状。另一个优势是该对象在世界各地的办公室中可用，以便于测试和验证我们的结果。我们使用域随机化技术生成数据，该技术还模拟了场景中的其他逼真的对象，称为分散对象。这些对象提供了视觉上的复杂性，遮挡和照明挑战，以帮助我们的模型在训练中获得鲁棒性。我们还释放了用于比较和评估合成数据集的手动收集数据集。该白纸提供了有力的证据，表明与手动捕获数据相比，现实世界应用可以将影像逼真的模拟数据用作更可扩展和灵活的解决方案。代码可在以下地址上找到：https：//github.com/datagenresearchteam/expo_markers
We show a straightforward and useful methodology for performing instance segmentation using synthetic data. We apply this methodology on a basic case and derived insights through quantitative analysis. We created a new public dataset: The Expo Markers Dataset intended for detection and segmentation tasks. This dataset contains 5,000 synthetic photorealistic images with their corresponding pixel-perfect segmentation ground truth. The goal is to achieve high performance on manually-gathered and annotated real-world data of custom objects. We do that by creating 3D models of the target objects and other possible distraction objects and place them within a simulated environment. Expo Markers were chosen for this task, fitting our requirements of a custom object due to the exact texture, size and 3D shape. An additional advantage is the availability of this object in offices around the world for easy testing and validation of our results. We generate the data using a domain randomization technique that also simulates other photorealistic objects in the scene, known as distraction objects. These objects provide visual complexity, occlusions, and lighting challenges to help our model gain robustness in training. We are also releasing our manually-gathered datasets used for comparison and evaluation of our synthetic dataset. This white-paper provides strong evidence that photorealistic simulated data can be used in practical real world applications as a more scalable and flexible solution than manually-captured data. Code is available at the following address: https://github.com/DataGenResearchTeam/expo_markers
