SVBRDF Database Bonn

Abstract

Details

Description

Here you can find a database of high quality measured materials collected by our group. In contrast to our BTF datasets, these materials are processed into more compact and efficient representations, namely spatially varying BRDFs (SVBRDFs). We use this dataset to improve the costly post-processing of the raw measurements, in particular the time consuming SVBRDF fitting, which we successfully accelerated to seconds instead of hours using a deep learning approach, trained on our UBOFAB19 dataset.

SVBRDFs are obtained by fitting parameters of analytical BRDF models (e.g. the Ward BRDF) to the individual pixels of image-based measurements. The fitting is conventionally performed by non-linear optimization, which is heavily based on heuristics and therefore not universally applicable in all scenarios. But more importantly, the optimization requires long processing times on the order of serveral hours for one material, sometimes up to half a day. To add some numbers, the cumulative processing times for the presented UBOFAB19 dataset are 56 days on a single workstation.

We develop alternative approaches to the SVBRDF fitting problem with the help of deep learning. In a first work we were able to reduce the processing times from several hours to a few minutes per material.

To spark further research, we make the training data for our methods publicly available.