Deep Geometric Functional Maps Robust Feature Learning For Shape Correspondence

Deep Geometric Functional Maps Robust Feature Learning For Shape Correspondence Information Guide

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LOGML - Maks Ovsjanikov: Robust learning-based methods for shape correspondence Update

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Information Shape Correspondence and Functional Maps Guide

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Michael Bronstein: Deep functional maps: intrinsic structured prediction...

Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence

Certifiable Outlier-Robust Geometric Perception: Robots that See through the Clutter with Confidence

Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching

DPFM: Deep Partial Functional Maps (3DV 2021)

Geometry and learning in shape correspondence problems

L5 Learning Geom d GH, LBO Optimality, Functional Maps

Shape analysis (spring 2019), Lecture 22: Shape correspondence

Robust Methods for Non-Rigid Spectral ShapeMatching by Robin Magnet

SGP 2020 Keynote – Maks Ovsjanikov

Introduction to Functional Maps

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Last Updated: September 19, 2026

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3DGV Seminar: Maks Ovsjanikov --- Robust and Efficient Geometric DL for Non-Rigid Shape Processing News

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Authors: Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov Description: We present a novel In this talk I will describe several recent works aimed at developing accurate and Authors: Farazi, Mohammad*; Zhu, Wenhui; Yang, Zhangsihao; Wang, Yalin Description: This paper studies 3D dense Short presentation of the 3DV 2021 paper: "DPFM: Speaker: Alex Bronstein, Technion, Israel WORKSHOP ON MACHINE Slides and other materials can be found here: groups.csail.mit.edu/gdpgroup/6838_spring_2019.html. Speaker: Robin Magnet (INRIA Paris) Abstract: This course provides an overview of modern methods for non-rigid

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