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Not all points are equal. There are always fundamental misconceptions about the type of data I am working with in my visualization research.

I work with particle data. This data is usually the result of simulations, e.g. generated through the methods of molecular dynamics or a discrete element method. The individual particles represent independent elements, e.g. atoms or mass centers, which are neither connected nor correlated. Of course, within the simulation these particles interact and influence each other, but with the pure data which is available for visualization to me, there is no topological structure between the particles at all. Literature was several further names for this kind of data: point-based data, mesh-less data, and, maybe the best fitting one, scattered data. Technically speaking, these data sets are arbitrarily sorted lists of elements, each storing a position and optional additional attributes, like a sphere radius or a color. But that is it. There are no more information and in general you cannot make any assumptions about structures within the data.

I now want to write about one very common misconception: there is the further data type of point clouds also called point-set surfaces. These data also consist of a list of points, which are at first not correlated. Typical sources of such data are point-based modeling in 3D computer graphics and, more common, scanning of real-world objects. Such scans would be created by laser scanners or structured-light scanners like the Kinect. Because of the fact that these data sets store a simple list of points, i.e. they are technically identical to particle data, results in the misconception of many people that these two types of data sets are identical. They are not.

Point clouds are discrete samplings of a continuous function, i.e. the surface of the scanned or modeled object. Therefore, all points reside, within some error margin, on this 2D surface embedded within the 3D space. This aspect is fundamentally different from particle data, in which the particles are freely placed throughout the 3D space. Almost every algorithm working with point-cloud data work with the assumption of the continuous surface represented by the points. As this assumption is not valid for particle data, these algorithms cannot be applied easily to this kind of data.

Well, obviously I have not published enough to make my colleagues in my field of science recognizes this difference. I am off then …

At the end of this week it was time for two important demo-days.

On Thursday the Output took place. Some kind of open house of the TU Dresden, at which scientific results and student’s works are presented to the public. Together with booths from industrial partners this mini-exhibition provides an overview of the output of the university. And this time, our demonstrators were particularly important. The junior research group VICCI, I am working in, is attached to the ResUbic lab, a cluster of multiple research groups working on the topics of Internet of things and cyber-physical systems. For many research groups within this is the last year of their funding period. Therefore, they understood output as their opportunity for a final presentation. So, they invited many important persons: industrial partners, press, and even government officials. Because of that, we put much effort into our demos, to make the best impression possible.

The best demo of VICCI, although I might be biased here, is the follower demo my colleagues Alex and André set up. It shows a combination of simple robot control and computer vision. To be more precise: the show a demo of object tracking. The robot is equiped with a camera, in our case a Kinect. With this sensor it lerns an object and then tracks this object within the camera’s image. In this photo we used a red coffee mug. The robot tries to keep a constant distant and a direct orientation towards this object. With this it is possible to remote control the robot by simply moving the mug correspondently.

Now, this demo is not that great because we can control a robot with a coffee mug. That is nice, but that is no rocket science. What is great is the object tracker. Alex uses a particle filter algorithms, based on probability distributions to assume the object position. This tracking is robust and very fast. A new object can be lerned within fractions of a second. Any object can be used, even bare hands.

On Friday the second demonstration came up directly after the Output: the long night of science (lange Nacht der Wissenschaft). We basically showed the same demonstrations as we did at the Output. Just some of the research groups of ResUbic did not want to. We did. We presented our work of VICCI to the broad public the whole evening and night, until the end of the day at 1 o’clock.

As conclusion I can say, that these two days were exhausting and that I am happy that this is finally over. Apart of that, our demonstrations were a complete success and the public liked what they saw.

I attended the EuroVis 2013 in Leipzig this week. It is the largest european conference on visualization. In my post about the annual meeting of the BZS I wrote that for scientific collaborations the small meetings are the best. An that is true. The EuroVis is nice, but is very hard to get in touch with people you don’t already know. But that is not a problem. The large conferences have other qualities.

The large conferences provide you with a good overview of the current state of the art in the corresponding field of science. They seek to select papers of high quality and, therefore, to present the most important scientific results. I don’t want to bad-mouth anyone or anything. There are always good papers and there are always paper I can only stand by, stare at and ask myself: WTF?

And that was exactly the case this year again. There were some good papers and I took some good ideas with me. Let’s see what I can make out of these.