论文标题
对照明模型对合成训练数据集产生的影响的研究
A study of the effect of the illumination model on the generation of synthetic training datasets
论文作者
论文摘要
当后者稀缺或昂贵时，使用计算机生成的图像来训练深神经网络是真实图像的可行替代方法。在本文中，我们研究渲染软件使用的照明模型如何影响生成的图像的质量。我们创建了八个培训集，每个培训集都具有不同的照明模型，并在三个不同的网络体系结构，Resnet，U-NET和我们开发的组合体系结构上对其进行了测试。测试集由用于生成训练集的相同CAD模型产生的3D打印对象的照片。渲染过程的其他参数的效果，例如纹理和摄像头位置。 我们的结果表明，照明模型的效果很重要，与网络体系结构的意义相当。我们还表明，两种光探针都捕获自然环境光和建模的照明环境，都可以给出良好的效果。在光探针的情况下，我们确定为影响性能的两个重要因素，即光探头和测试环境之间的相似性以及光探针的分辨率。关于建模的照明环境，与测试环境的相似性再次被确定为重要因素。
The use of computer generated images to train Deep Neural Networks is a viable alternative to real images when the latter are scarce or expensive. In this paper, we study how the illumination model used by the rendering software affects the quality of the generated images. We created eight training sets, each one with a different illumination model, and tested them on three different network architectures, ResNet, U-Net and a combined architecture developed by us. The test set consisted of photos of 3D printed objects produced from the same CAD models used to generate the training set. The effect of the other parameters of the rendering process, such as textures and camera position, was randomized. Our results show that the effect of the illumination model is important, comparable in significance to the network architecture. We also show that both light probes capturing natural environmental light, and modelled lighting environments, can give good results. In the case of light probes, we identified as two significant factors affecting performance the similarity between the light probe and the test environment, as well as the light probe's resolution. Regarding modelled lighting environment, similarity with the test environment was again identified as a significant factor.
