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VERANSTALTUNGEN

Paper published in PFG: Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering

Examples for summer (middle) and winter (right) representation of the same input point cloud coloured by reflectance (left) in Hannover

Torben Peters and Claus Brenner developed a method to create photorealistic visualizations from point clouds.

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.

 

link.springer.com/article/10.1007/s41064-020-00114-z

NEWS

Paper published in PFG: Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering

Examples for summer (middle) and winter (right) representation of the same input point cloud coloured by reflectance (left) in Hannover

Torben Peters and Claus Brenner developed a method to create photorealistic visualizations from point clouds.

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.

 

link.springer.com/article/10.1007/s41064-020-00114-z