An overview of general trends in Geometry Processing

In this talk I will give an introduction to the field of Geometry Processing: how to process a 3D shape from early tasks: denoising, super-resolution or upsampling and surface reconstruction to high level tasks such as shape recognition or shape editing. From traditional axiomatic methods to more recent deep learning developments including implicit neural representations, the field has undergone some radical changes over the recent years. While deep learning for regular euclidean data has led to a huge leap in performance for image analysis and image generation, the progress is not as impressive for shape analysis or shape generation.  This is largely due to the challenges posed by non-euclidean data, which require special dedicated architectures, often not as efficient and widely spread as image ones, and this talk will present some techniques addressing these challenges.