The english of the teachers is difficult to understand. Is good to know that you can become a professor of Penn university with that english, but to be in front of a camere they should select somebody understandable.

The level of the course is way too difficult. I have a bachelor in computer science, a master in computer science and a bachelor in mathematics, so I don't think the problem is the material, but how it's teached. You are supposed to solve very complicated assigments just with the videos and that's a too big challenge. They don't spend the time with examples and detailed approach.

Thanks god that the course has been active for several years and there's enough help in the forums. By the way, people in the forums complaint about the poor support of the teachers and assistants in the forums.

Very bad course in general.

Sloppy, unorganized, and poorly presented. Try the similar class on EdX and YouTube for a much better and clear explanation and presentation.

The least comprehensive course I ever took. It needs very good prerequisite about computer vision. And the demostration was very poor.

Great course for those who wants to understand how classical SLAM systems work. I think it would be a bit more practical if the assignments were made in python.

The assignments and weekly work is by far much more than the hours estimated. Finally I feel as I didn't learn anything.

This is quite challenging course. So far, this is the course with the largest amount of material, I wish the class will be split into two courses.

The content is very good but the lecture presentation and structure could use improvement. Assignment instructions are not as clear as they could be.

Lack of derivation on most of the mathematics. Poor explanation on half of the topics. Lack of diagrams or animations as s supplementary for some complicated subjects. Poor-designed programming assignments which fail to cover some of the topics mentioned in the slides

For Computer Vision enthusiast who wants to learn about Multiple View Geometry, this is the best beginner course

I took this course as I wanted to develop more skills in computer vision. To me the course seems to be an abridged upper-year college course. The course presumes a strong linear algebra understanding, as the instructors didn't spare much time on explaining the linear algebra concepts. Therefore a refresher for linear algebra as a course handout would be very helpful. The first half of the course are really good - instructions are clear and assignments and quizes are instrumental to understanding. However, progressing to the second half of the course, the instructors rushed to different concepts without properly linking them. And you can tell the disorganization from the way they present - they tend to pause intermittently, repeat the same content, speak grammatically incorrectly. There are significantly more typos in the course notes as well. I would give a 5-star if the course is of consistent good quality, but unfortunately it is not.

The videos should be completely reconsidered. Instructors pointing at virtual points on screen does not help at all. The course creators tried to squeeze in many concepts in 4 weeks. More examples should be provided in lectures, not just abstract math (e.g. lectures on projective transformation).

The English of the teachers is god awful and the explanations are even worse. Honestly do not recommend this course. Try instead the perception course at the U of Toronto self-driving car specialization.

I can barely understand what professor Kostas Daniilidis, because of his accent. And, the audio of professor Kostas Daniilidis is always very small. It makes us difficult to hear from him.

The material in this course is explained very poorly.

Very bad communication pronunciation and grammar

An *excellent* but tough course! I really struggled with it because it's a little out of my expertise (I'm an engineer with two master's degrees and equivalent of almost a half degree in math, , but descriptions of the perspective manipulations are novel to me). I managed to do the work by watching the videos several times and reading the forums. I prolonged the course by continuing to pay the fee for quite a few months. This last assignment was very difficult, but I finally sat down and worked through it - scored 50%, then maybe 67%, and then 84% (passed) . I'm disappointed that the course seemed to accept that as the final submission, and I think I'm being forcibly "graduated" and advanced to the next class. :) I actually want to get the final NonLinearTriangulation part of the routine fully working and maybe earn 100%, but I guess I will do that on my own time. Also, I'd like to comment on the forums as to some specific methods I used to successfully complete the last assignment. I am looking forward to the next class (5 of 6 ) being much easier for me, as the material looks a lot like things I've already covered.

Excellent organization and presentation of the course material, and very prompt responses from the teaching staff on the message boards.

Very good course. The only thing that I would suggest is an higher precence of moderators in the forum. It would be very appreciated.

good materials for visual SLAM

Lots of cutting edge mathematical and computer vision concepts, but a serious lack of support. No technical staff around to help, only one or two examples of how the math works, and a general poor explanation of the concepts.

This course is not for beginners, and I recommend you steer clear if you do not posses at minimum a bachelors in mathematics.

Compared to other courses I've taken on Coursera (15+) this was very poor quality - both the content and presentation. I wish there were better a better course on this topic on Coursera.

It's very difficult to understand the lecturers both in term of language and clearity of explanations.

To difficult to follow.

A BRILLIANT, BRILLIANT COURSE, the teachers put A LOT of effort into making the lecture slides and videos. Everything was explained multiple times such that the student understands it better. Also, computer vision, especially geometric vision is difficult to understand without a proper background in linear algebra, but the teachers' explanation was enough to fill the gaps so that even someone with only a minimalistic knowledge of linear algebra was able to consume the content.

The exercises were a class apart, they were very well structured with tangible results at the end of each, And each of the exercises brought together the key points of the lectures, so that the student could easily implement them in code and test out the algorithm.

Last but the not the least, the community was very active with the teaching assistants pitching in wherever necessary, in particular, Stephen did a great job of understanding the issues students were facing and taking appropriate action.

All in All, a very well structured course to jump start one's career into computer vision.

It is certainly the most comprehensive course in computer vision that can be provided in a span of four weeks. It is much time consuming compared to the other four courses (I have done this at the end); however, each and every bit of it is worth it. The teaching is incredible, especially, Prof. Shi's teaching includes intuition and physical interpretation which helps in appreciating the equations much more. The assignments equally match with the lecture content. Trust me, by the end of four weeks you will be comfortable in reading and understanding papers in visual SLAM, pose estimation, etc.

A small suggestion: in a few lectures, for instance in SIFT lecture, Prof. Daniilidis is not shown in the screen whereas his actions are necessary to better understand the content in the slides.

The Professors and the TAs have done a commendable job and thank you all for this course.