We investigate whether conditional generative adversarial networks (C-GANs) are suitable for point cloud rendering. For this purpose, we created a dataset containing approximately 150,000 renderings of point cloud–image pairs. The dataset was recorded using our mobile mapping system, with capture dates that spread across 1 year. Our model learns how to predict realistically looking images from just point cloud data. We show that we can use this approach to colourize point clouds without the usage of any camera images. Additionally, we show that by parameterizing the recording date, we are even able to predict realistically looking views for different seasons, from identical input point clouds.

我们调查条件生成对抗网络（C-GAN）是否适合点云渲染。为此，我们创建了一个数据集，其中包含大约150,000个点云图像对的渲染。该数据集是使用我们的移动地图系统记录的，捕获日期分布在1年内。我们的模型学习如何仅从点云数据中预测逼真的图像。我们证明了可以使用这种方法为点云着色，而无需使用任何相机图像。此外，我们表明，通过参数化记录日期，我们甚至可以从相同的输入点云中预测不同季节的逼真视图。