I’ve been curating equipment (and photos) of electronics. Here’s my top 12:
It’s a secret for now.
Run your computer for long enough and you may notice the process /usr/bin/X begins to slowly consume all of your memory.
Well, it’s a known bug. The workaround at the moment comes from Brian Trotter:
Reverting from the fglrx (proprietary) driver to the xserver-xorg-video-ati (open source, tested) driver solved the problem for me.
Going back to the generic driver works too, but then I can’t overclock. This is one I’m choosing to live with until ATI/AMD provides an update.
This is a quick 1:22 video about my Arduino desk clock I posted below. These build videos will get better with time and practice, I promise
I’ve started a new weekly series of blog posts called Zen. I’ve spent a lifetime focusing on my work, but never really put other aspects of my life under a magnifying glass before. Hopefully I can change that, and maybe help a person or two.
I’m toying with the idea of making a video/week that showcases my work. I haven’t made the commitment yet, but here’s my first shot, and a glimpse of what that might look like.
There is an abundance of posts about Quadrilateralized Spherical Cubes (QLSC) online, but I can’t find any that are sticking to the true nature of Dr. Chan’s original work. It’s a fascinating anomaly that has spread and evolved in the past 6 years, most likely by accident. I contributed to it by making the same exact mistake and had to later publish errata.
Anyway, I love talking about it and the posts seem popular so I would like to share a recent response I made to someone who wrote in. I think it may give just a little bit more detail on what’s going on here. If you want to chime in please feel free to add your comments or write to me too!
Clipped for brevity. Start at the third paragraph for QLSC-only info.
I’m still using the OpenGL2 pipeline for that project. I initially generate my points using the polar coordinate system. Every point has the same distance from the center of the sphere but different angles. If I wanted more points for more detail, I can average the angles of 2
existing points to add a third point directly between them. Once all those points are generated they are converted to Cartesian (x,y,z) and fed into the vertex buffer to be rendered.
(A side note: You could then animate or alter the terrain of the earth by just changing the distance of each point in the polar coordinate system and that would translate all the way into the final earth mesh!)
I couldn’t find a whole lot of information about Ken’s algorithm either, but I did find is the QLSC algorithm was actually for taking points on a virtual spheroid and making it accurately match up with flat hierarchies of data. I.e., unraveling a sphere into a flat surface to map out some data; and the reverse, taking flat data and mapping it accurately to a sphere. He usually stresses that this is an equal-area mapping algorithm. I don’t really know a whole lot about the specifics here, his papers are hard to read and understand.
He sent me an email after writing his comment, saying it was mostly for stuff like the Cosmic Background Explorer at NASA. They had to study where exactly radiation was coming from and they couldn’t tolerate even small distortions when tracking and mapping this data or it would ruin scientific data crunching. I prodded him for more information but he never got back to me. The guy’s probably super busy, so I’m glad he took the time to message me in the first place.
The lesson I learned is that I wasn’t really using his research at all! Sad, but true. Cubespheres were used well before Ken was born, by cartographers making interesting and wonderful world maps. And that’s what I ended up replicating in my work.
If you look online you will find a lot of other people thought they were using the QLSC algorithm too, but in fact they were just making plain-old cubespheres (or sometimes called quadspheres), and they generally store all their terrain height data in a quadtree data structure, which is better suited for graphics stuff.
I made another article to note my corrections but I left the old one intact because I wanted to keep the comments Ken made. I think because so many people were calling this a QLSC–the name really does sound cooler–it caught on quickly and became an artifact of errant
reality. I think it’s neat.
Thanks for writing to me,
In essence, if you’re looking to do planetary rendering, you’ll want Quadspheres/Cubespheres and Quadtrees specifically; QLSC probably won’t help you. In fact, there are a few solutions out there already, for Ogre or Outerra. UE4 has a wonderful landscape rendering engine that can probably be employed to do planetary rendering if that better suits your needs. UE4 is also pretty.
One last note, NASA also has some really cool work-in-progress for planetary rendering, and a friend has been actively deriving from that work, while another is responsible for the physics simulation behind it. I’m not sure if I can provide any details, but I’m excited about these new advancements in space simulation. I will update this post if I get permission to post snapshots or links and such.
As always, hope this helps.
You may be experiencing the following while using Chrome under Ubuntu:
- Poor system responsiveness
- High disk usage
- Running out of physical memory, and high swap usage
- Corrupted profile when launching Chrome
- Crashes from other processes (happens from low memory conditions)
- Even after closing Chrome, processes stay in memory and continue to eat up RAM.
Well, the easy fix is pretty simple; just install the beta channel instead!
sudo apt-get install google-chrome-beta
I’m not sure for the reason behind this, but the stable release seems to have memory leak and file I/O issues.
Recently a reader wrote in regarding my earlier Quadrilateralized Spherical Cubes post, however I had neglected to update the original post with some added corrections. I initially wrote about this topic for my first dip into 3D visualization, and unfortunately at the time I conflated QuadSpheres and Quadrilateralized Spherical Cubes as a single concept. Both are a popular topic for visualization, game making and scientific research so I’m going to expand a little bit further on the differences between the two and hopefully that will make it a little bit clearer.
First let me preface this by saying that there’s more than one way to generate a mesh for a sphere. Since my intention was to reduce visual distortion on the earth texture I was using, I chose to map my textures to a QuadSphere, rather than the common method of using ECP tesselation which causes pinching at the north and south poles of the spheroid. A QuadSphere is composed of 6 faces which correlate to the 6 sides of a cube. These faces are called quads because they are essentially squares before you begin any transformations on them. Each of these 6 faces can be subdivided further into smaller quads to increase detail when the camera is closer to the mesh, and each of those resultant quads are tessellated into tris and rendered. In a modern graphics engine this will take place in the vertex shader stage of the rendering pipeline, so you will just feed in the heightmaps and shaders to shape your terrain.
For an older OpenGL 2.0-style pipeline, you can create a QuadSphere by projecting equidistant rays starting from your origin of the sphere to the surface and pushing those vertices into a buffer. This can start out as a 32 x 32 curved grid or something; 1 grid for each side; 6 grids in total to form the spherical shape. You can further subdivide these grids to get more detail when your camera is closer. That’s a QuadSphere in a nutshell.
My work never used the QLSC agorithm. A QLSC ensures that all virtual points on your map take up an equal area of space, decreasing the amount of distortion. Not necessarily texture distortion, but aberration from your virtual model to the real life thing. Distortion occurs naturally when projecting flat data onto a spheroid, so Doctor Kenneth Chan worked extensively on the QLSC algorithm to make it extremely accurate at mapping points and data. This is great for scientific research and that is why it was used in the Cosmic Background Explorer project at NASA.
There are a lot of little caveats to this, but the big takeaway is there’s not a huge need for QLSC in real-time visualization and games. You’ll likely need it for data processing, geomapping spatial data, or other areas of scientific interest.
One last term to take note of is cube mapping. That is how a texture is mapped onto a shape consisting of quads, but not necessarily how that texture will be projected. For instance, skyboxes are meant to be projected on a cube, not a QuadSphere. Though both of these are cube mapped, you will need properly projected textures to suit your needs to minimize the amount of texture distortion.
Have any questions or corrections? Please leave it in the comments below and I’ll be glad to elaborate more.