SDF Geometry Processing

The historical focus of the Computer Graphics research community has had a deep influence on the representations chosen to store and process geometric information. Beyond the classical explicit formats like meshes and point clouds; in this talk, I will argue that Signed Distance Functions (SDFs) are a preferable representation for many tasks in engineering, robotics and machine learning. I will review recent work on reconstructing surfaces from SDFs and share an exciting vision for a future of geometric deep learning that exploits all the geometric information contained in each SDF sample.