Barricadenick
New Member
Last edited:
Intro GSoC: Gaskarov Airat
- Thread starter Barricadenick
- Start date
Hi Airat and welcome!
I've seen you already forked the project on GitHub. Did you manage to get it running from source as well? If you have questions, just hop on the IRC chat and you'll likely find somebody who can help you if you run into problems.
If you want to get into GSOC with us this year you should read through the task list and figure out what interests you the most. Is there anything particular you'd like to work on?
I've seen you already forked the project on GitHub. Did you manage to get it running from source as well? If you have questions, just hop on the IRC chat and you'll likely find somebody who can help you if you run into problems.
If you want to get into GSOC with us this year you should read through the task list and figure out what interests you the most. Is there anything particular you'd like to work on?
Barricadenick
New Member
Hello, Skaldarnar
I noticed an interesting task in the list called "Noise-based generation of distinguishable terrain features". I had an experience with terrain generation for 2d case so that's why I'd like to try to solve this problem.
I noticed an interesting task in the list called "Noise-based generation of distinguishable terrain features". I had an experience with terrain generation for 2d case so that's why I'd like to try to solve this problem.
Hybrid
New Member
I figured I'd add my 2 cents here.
@Barricadenick I hope you understand that working in 2d and working in 3d are quite different. You can get away with somewhat nonsenical things in 2d, but things need to make more sense in 3d.
Do you have any first impressions on how to solve this? I considered trying this one, but I got nowhere and dropped it. My first impression was a generative adversarial network, but Terasology doesn't have a ML framework yet, and implementing one would be far beyond GSoC scope.
Also, hurry. GSoC applications are already open, and you need to design your solution and convince the team about it.
@Barricadenick I hope you understand that working in 2d and working in 3d are quite different. You can get away with somewhat nonsenical things in 2d, but things need to make more sense in 3d.
Do you have any first impressions on how to solve this? I considered trying this one, but I got nowhere and dropped it. My first impression was a generative adversarial network, but Terasology doesn't have a ML framework yet, and implementing one would be far beyond GSoC scope.
Also, hurry. GSoC applications are already open, and you need to design your solution and convince the team about it.
The world generation tutorial would be the best starting point. You can solve quite a lot by just using and combining 2D noise functions but, as @Hybrid points out, sooner or later you'll have to look at the 3D case. Fortunately, our faceted world generator concept allows for a lot of flexibility. In addition, we have a variety of noise functions already implemented. Just customize the parameters to fit your needs and start using them!
We also had a bunch of world gen related tasks in Google Code-in this year. Have a look for inspiration and how to get started: https://codein.withgoogle.com/tasks/?sp-organization=5452182442737664&sp-categories=1&sp-search=world gen
We also had a bunch of world gen related tasks in Google Code-in this year. Have a look for inspiration and how to get started: https://codein.withgoogle.com/tasks/?sp-organization=5452182442737664&sp-categories=1&sp-search=world gen