Learning Detail Transfer based on Geometric Features

We present an approach to object detection and recognition in a digital image using a classification method that is based on the application of a set of features that include

Our approach is based on the observation that many geometric feature based shape descriptors—also global ones—can be localized in the fol- lowing sense: first the shape is

Since the gross-scale 3D reconstruction is not able to reliably capture albedo and meso-scale surface detail, we acquire a set of material appearance models that are representative

Content-based shape retrieval using the shape itself as a query and based on the comparison of geometric and topo- logical properties of shapes is complicated by the fact that many

The geometric description is based on a statistical technique to select properties that are mutually independent; the photometric information is handled according to a

2017 The Author(s) Computer Graphics Forum c 2017 The Eurographics Association and John Wiley & Sons Ltd..c. Berkiten

The deep learning based image comparison metric allows us to start with a population of primitive rendering parameters (i.e. transfer functions), and stochastically iterate the

In this chapter, we provide an experimental evaluation of the performance of intrinsic CNN methods in the problem of learning shape correspondence across shapes from