19 - Self-supervised Learning for 3D Shape Analysis/ClipID:34618 previous clip next clip

Recording date 2021-06-18

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Producer

Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Format

lecture

Daniel Cremers on "Self-supervised Learning for 3D Shape Analysis":

While neural networks have swept the field of computer vision and replaced classical methods in most areas of image analysis and beyond, extending their power to the domain of 3D shape analysis remains an important open challenge. In my presentation, I will focus on the problems of shape matching, correspondence estimation and shape interpolation and develop suitable deep learning approaches to tackle these challenges. In particular, I will focus on the difficult problem of computing correspondence and interpolation for pairs of shapes from different classes — say a human and a horse — where traditional isometry assumptions no longer hold.  

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