Hi
I’ve been working feverishly to straighten up the Structure from Motion Toy Library, and make it more robust. During my experiments with different methods I wanted to test out a different method for decomposing the Essential matrix to rotation R and translation t, other than that of Hartley and Zisserman using SVD. That’s when I came upon this paper: here by Berthold Horn from 1990, that traces the steps of Longuet-Higgins who came up with the derivation for the Essential matrix. It has a closed form solution that works pretty well, and here it is implemented with the Eigen math library (a very good library to get to know).
Category: opencv
Hello
This time I’ll discuss a basic implementation of a Structure from Motion method, following the steps Hartley and Zisserman show in “The Bible” book: “Multiple View Geometry”. I will show how simply their linear method can be implemented in OpenCV.
I treat this as a kind of tutorial, or a toy example, of how to perform Structure from Motion in OpenCV.
See related posts on using Qt instead of FLTK, triangulation and decomposing the essential matrix.
Update 2017: For a more in-depth tutorial see the new Mastering OpenCV book, chapter 3. Also see a recent post on upgrading to OpenCV3.
Let’s get down to business…
Hi
I sense that a lot of people are looking for a simple triangulation method with OpenCV, when they have two images and matching features.
While OpenCV contains the function cvTriangulatePoints in the triangulation.cpp file, it is not documented, and uses the arcane C API.
Luckily, Hartley and Zisserman describe in their excellent book “Multiple View Geometry” (in many cases considered to be “The Bible” of 3D reconstruction), a simple method for linear triangulation. This method is actually discussed earlier in Hartley’s article “Triangulation“.
I implemented it using the new OpenCV 2.3+ C++ API, which makes it super easy, and here it is before you.
Edit (4/25/2015): In a new post I am using OpenCV’s cv::triangulatePoints() function. The code is available online in a gist.
Edit (6/5/2014): See some of my more recent work on structure from motion in this post on SfM and that post on the recent Qt GUI and SfM library.
Update 2017: See the new Mastering OpenCV3 book with a deeper discussion, and a more recent post on the implications of using OpenCV3 for SfM.
Hi!
I’ve been working on implementing a face image relighting algorithm using spherical harmonics, one of the most elegant methods I’ve seen lately.
I start up by aligning a face model with OpenGL to automatically get the canonical face normals, which brushed up my knowledge of GLSL. Then I continue to estimating real faces “spharmonics”, and relighting.
Let’s start!
Hi!
I would like to present something I have been working on recently, a work that immensely affect what I wrote in the blog in the past two years…
To use it:
Go on this page,
Watch the short instruction video,
download the application (MacOSX-Intel-x64 Win32)
and make yourself a model!
It takes just a couple of minutes and it’s very simple…
This work is an academic research project, Please please, take the time to fill out the survey! It is very short..
The results of the survey (the survey alone, no photos of your work) will possibly be published in an academic paper.
Note: No information is sent anywhere in any way outside of your machine (you may even unplug the network). All results are saved locally on your computer, and no inputs are recorded or transmitted. The application contains no malware. The source is available here.
Note II: All stock photos of models used in the application are released under Creative Commons By-NC-SA 2.0 license. Creator: http://www.flickr.com/photos/kk/. If you wish to distribute your results, they should also be released under a CC-By-NC-SA 2.0 license.
Thank you!
Roy.
I want to share a small piece of code to do Laplacian Blending using OpenCV. It’s one of the most basic and canonical methods of image blending, and is a must exercise for any computer graphics student.
Just wanted to share of some code I’ve been writing.
So I wanted to create a food classifier, for a cool project down in the Media Lab called FoodCam. It’s basically a camera that people put free food under, and they can send an email alert to the entire building to come eat (by pushing a huge button marked “Dinner Bell”). Really a cool thing.
OK let’s get down to business.
Hi,
I wanted to put up a quick note on how to use Kalman Filters in OpenCV 2.2 with the C++ API, because all I could find online was using the old C API. Plus the kalman.cpp example that ships with OpenCV is kind of crappy and really doesn’t explain how to use the Kalman Filter.
I’m no expert on Kalman filters though, this is just a quick hack I got going as a test for a project. It worked, so I’m posting the results.
I found a nice little trick to ease the work with the very noisy depth image the Kinect is giving out. The image is filled with these “blank” values that basically note where the data is unreadable. The secret is to use inpainting to cover these areas and get a cleaner image. And as always, no need to dig deep – OpenCV has it all included.
Hi
Just wanted to share a thing I made – a simple 2D hand pose estimator, using a skeleton model fitting. Basically there has been a crap load of work on hand pose estimation, but I was inspired by this ancient work. The problem is setting out to find a good solution, and everything is very hard to understand and implement. In such cases I like to be inspired by a method, and just set out with my own implementation. This way, I understand whats going on, simplify it, and share it with you!
Anyway, let’s get down to business.
Edit (6/5/2014): Also see some of my other work on hand gesture recognition using smart contours and particle filters