RIFNOM: 3D Rotation-Invariant Features on Normal Maps

3D Model Retrieval Based on Greedy Search by Xiamen University (XMU-GS/XMU-GS-FB) In this method, three types of features are extracted for each image, including 49-D Zernike

Based on the analyses in [LGX13], existing non-rigid 3D shape retrieval methods can be roughly classified into al- gorithms employing local features, topological

A method is proposed to identify and localize semantic features like anatomical characteristics or decorations on digital arte- facts or fragments, even if the features are

We showed that there is no one global set of geometric features to guide the surface detail transfer across different shape categories so a metric learning algorithm is needed

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

The Cumulative Maps (C-maps) are similar to the SWD-map: given an image, we use the visual features computed by VSFM to esti- mate a perspective transform T between the reference

We assign a local coordinate system (CS) to each pixel by using neighbor normals to extract the 3D rotation-invariant features.. These features can be used to perform interest

Our goal is to include topological geographic features such as a river in a Dorling-style or rectangular cartogram to make the visual layout more recognizable, increase map