EPN-V2

ACIT4030 Machine Learning for 3D Computer Vision Emneplan

Engelsk emnenavn
Machine Learning for 3D Computer Vision
Studieprogram
Master's Programme in Applied Computer and Information Technology
Omfang
10.0 stp.
Studieår
2022/2023
Pensum
HØST 2022
Timeplan
Programplan
Emnehistorikk

Innledning

This course will present the state of the art in algorithms for machine learning on images and 3D data. After a brief introduction to;3D geometry, we will cover topics related to deep learning for 3D data. We will in particular study deep neural architectures for;3D data such as point clouds, images, and;shape graphs.The course covers applications like classification, segmentation, shape retrieval and correspondence detection. Recent work on shape synthesis and joint embedding;will also be discussed.

Anbefalte forkunnskaper

A background in programming, machine learning, and linear algebra is an advantage. Knowledge of computer graphics and image processing is preferable, but not strictly required.

Forkunnskapskrav

No formal requirements over and above the admission requirements.

Læringsutbytte

Upon successful completion of the course, the candidate:

Knowledge

  • has knowledge of problems within graphics and imaging that are applicable to machine learning, such as classification, segmentation, correspondence detection, and shape retrieval.
  • has a good understanding of problems related to shape synthesis.

Skills

  • is able to apply state-of-the art machine learning algorithms to real-world problems related to imaging and 3D graphics.

Competence

  • is aware of the state of the art in algorithms for machine learning on;3D data.
  • has experience with real world problems within the course domain, with a focus on solutions using deep neural architectures.

Innhold

  • Introduction to;geometric modelling
  • Convolutional neural networks in 3D
  • Deep learning for point clouds
  • Convolutional neural networks on graphs
  • 3D synthesis;and analysis
  • Joint embedding for images and 3D data

Arbeids- og undervisningsformer

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Arbeidskrav og obligatoriske aktiviteter

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Vurdering og eksamen

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Hjelpemidler ved eksamen

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Vurderingsuttrykk

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Sensorordning

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Emneansvarlig

Associate Professor Henrik Lieng