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Evangelos Kalogerakis
Researcher at University of Massachusetts Amherst
Publications - 84
Citations - 10458
Evangelos Kalogerakis is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Point cloud & Shape analysis (digital geometry). The author has an hindex of 33, co-authored 76 publications receiving 8276 citations. Previous affiliations of Evangelos Kalogerakis include University of Toronto & Stanford University.
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Proceedings ArticleDOI
Multi-view Convolutional Neural Networks for 3D Shape Recognition
TL;DR: In this article, a CNN architecture is proposed to combine information from multiple views of a 3D shape into a single and compact shape descriptor, which can be applied to accurately recognize human hand-drawn sketches of shapes.
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Multi-view Convolutional Neural Networks for 3D Shape Recognition
TL;DR: This work presents a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and shows that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art3D shape descriptors.
Journal ArticleDOI
Learning hatching for pen-and-ink illustration of surfaces
TL;DR: This article presents an algorithm for learning hatching styles from line drawings, which can be generated in the artist's style by synthesizing hatching strokes according to the target properties.
Proceedings ArticleDOI
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Hang Su,Varun Jampani,Deqing Sun,Subhransu Maji,Evangelos Kalogerakis,Ming-Hsuan Yang,Jan Kautz +6 more
TL;DR: A network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice that outperforms existing state-of-the-art techniques on 3D segmentation tasks.
Journal ArticleDOI
Learning 3D mesh segmentation and labeling
TL;DR: This paper presents a data-driven approach to simultaneous segmentation and labeling of parts in 3D meshes, formulated as a Conditional Random Field model, with terms assessing the consistency of faces with labels, and terms between labels of neighboring faces.