More Than Technical

Archive for the ‘3d’ Category

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.

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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!
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A very simple thing, although I couldn’t find on Google some place to copy-paste off, so here it is:
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I’ve seen some examples of people who build motion parallax capable screens using Kinect, but as usual – they don’t share the code. Too bad.
Well this is your chance to see how it’s done, and it’s fairly simple as well.
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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.
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Hi

Another quicky on how to use Kinect (libfreenect) with OpenCV 2.1. I already saw people do it, but havn’t seen code.

UPDATE (12/29): OpenKinect posted very good C++ code of using libfreenect with OpenCV2.X APIs: here it is. Plus, their git repo now has a very clean C code: here it is.

So here it goes
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Hi,

Just wanted to share a bit of code using OpenCV’s camera extrinsic parameters recovery, camera position and rotation – solvePnP (or it’s C counterpart cvFindExtrinsicCameraParams2). I wanted to get a simple planar object surface recovery for augmented reality, but without using any of the AR libraries, rather dig into some OpenCV and OpenGL code.
This can serve as a primer, or tutorial on how to use OpenCV with OpenGL for AR.

The program is just a straightforward optical flow based tracking, fed manually with four points which are the planar object’s corners, and solving camera-pose every frame. Plain vanilla AR.

Well the whole cpp file is ~350 lines, but there will only be 20 or less interesting lines… Actually much less. Let’s see what’s up
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Hi

Just wanted to share a small thing I did with OpenCV – Head Pose Estimation (sometimes known as Gaze Direction Estimation). Many people try to achieve this and there are a ton of papers covering it, including a recent overview of almost all known methods.

I implemented a very quick & dirty solution based on OpenCV’s internal methods that produced surprising results (I expected it to fail), so I decided to share. It is based on 3D-2D point correspondence and then fitting of the points to the 3D model. OpenCV provides a magical method – solvePnP – that does this, given some calibration parameters that I completely disregarded.

Here’s how it’s done

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Hi everyone

In the last weekend I attended GeekCon 2009, a tech-conference, with a friend and colleague Arnon (not Arnon from the blog, who recently had a birthday – Happy B-Day Arnon!). Each team that attended had to create a project they can complete in 2-days of the conference. Our project is called “RunVas”, and the basic idea was to let people run around and paint by doing so. We wanted to combine computer vision with a little artistic angle.

Here’s some more details
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Hi

The graphics course I took at TAU really expanded my knowledge of 3D rendering, and specifically using OpenGL to do so. The final task of the course, aside from the exam, was to write a 3D game. We were given 3 choices for types of games: worms-like, xonix-like and lightcycle-like. We chose to write our version of Worms in 3D.

I’ll try to take you through some of the problems we encountered, the decisions we made, and show as much code as possible. I’m not, however, gonna take you through the simple (yet grueling) work of actually showing meshes to the screen or moving them around, these subjects are covered extensively online.

The whole game is implemented in Java using JOGL and SWT for 3D rendering. The code is of course available entirely online.

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