ACIT4030 Machine Learning for 3D Computer Vision Course description

This course will present the state of the art in algorithms for machine learning for the 3D environment. We will cover topics related to deep learning for 3D data such as point clouds, multi-view images, and shape graphs. The course covers applications like classification, segmentation, shape retrieval and scene representation.

Pass or fail.

No formal requirements over and above the admission requirements.

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 3D shape and image 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.

Teaching approach is a combination of traditional weekly lectures and practical work on a semester group project. Lectures will present influential research for relevant topics. The semester group project exposes the student to a chosen real-world problem relevant to the course topic.

Practical training

The student will be exposed to programming with repositories such as ShapeNet and will have created solutions for real-world problems related to deep learning for 3D data.

None.

The exam consists of three parts:

1. Oral presentation of 15 minutes (20% of the final grade), individual or in a group of two
2. Written evaluation of another student presentation, 500-1000 words (10% of the final grade), individual or in a group of two
3. Final project report between 6000 and 11,000 words (70% of the final grade), individual or in a group of two.

All three parts of the exam must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

The course will consist of lectures. In conjunction with the lectures, students will complete exercises related to the lecture topic. A compulsory assignment will be given to the students, to be presented to the other students on the course.

Practical training

The students will be exposed to practical exercises in evidence-based engineering. These exercises will be tailored to the topics that have been discussed and lectured on.

The following required coursework must be approved before the student can take the exam:

Prior to examination it is required that a compulsory assignment be completed, presented to the other students, and approved.

The exam itself is a report written on a self-selected estimation topic. The structure and content of the report should follow evidence-based principles and require searching for, evaluating and summarising practice and research-based evidence.

The exam can be appealed.

All aids are permitted.