Niko's Project Corner

Global illumination

I im­ple­mented a sim­ple global il­lu­mi­na­tion al­go­rithm, which con­structs and solves the sparse ma­trix which de­scribes how much tiles re­flect light to each other. It sup­ported only gray-scale ren­der­ing and wouldn't scale up to big­ger sce­nes, but it was nev­er­the­less an in­ter­est­ing pro­ject and I learnt a lot about 3D ge­om­etry, lin­ear al­ge­bra and com­puter graph­ics. Graph­ics was drawn by a soft­ware ren­derer which uses only stan­dard SDL prim­itive draw calls.

CUDA realtime rendering engine

So far I've writ­ten a ba­sic ren­der­ing en­gine which uses Nvidia's CUDA (Com­pute Uni­fied De­vice Ar­chi­tec­ture) which can ren­der re­flec­tive sur­faces with en­vi­ron­men­tal map­ping and anti-alias­ing and mo­tion blur at 200 fps with min­imal us­age of 3^rd party li­braries such as OpenGL. This let me fully im­ple­ment the cross-plat­form ren­der­ing pipeline from data trans­fer to pixel-level RGB cal­cu­la­tions, all in C-like syn­tax.

Visualizing laser scanned geography

Dur­ing the sum­mer of 2012 when I was mainly work­ing on my Mas­ter's The­sis, I also had a look at Na­tional Land Sur­vey of Fin­land's open data down­load ser­vice. There I down­loaded a point cloud dataset which had typ­ically 4 - 5 mea­sured points / square me­ter. This means that to vi­su­al­ize a re­gion of 2.5 × 2 km, I had to work with a point cloud con­sist­ing of 5 × 2500 × 2000 → 25 mil­lion points. I chose to con­cen­trate on my cam­pus area, be­cause I know it well and it has many in­ter­est­ing land­marks. For ex­am­ple the iconic main build­ing can be seen in Fig­ure 1.