Joran Vandenbroucke

Welcome to my Portfolio.

I am a C++ Engeneer who mainly focuses on Game Engine, Game AI, and Graphics Programming

Who am I?

I am a recent Master graduate.
My Master Thesis is about size extraction in Navigation Mesh Generation. The size extractions allows for a single navmesh to be generated but used by many agents with different radii.

C/C++; C#; GLSL; HLSL

CMake; CLion; Visual Studio; Nsight; RenderDoc; VTune

Vulkan; OpenGL; DX11

SDL2/3; Catch2; ImGui; GLM; GLI; mikktspace; ASSIMP

VCS: Git; Perforce; Mercurial

Trello; Jira

Unity; Unreal Engine

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Read more (Curriculum Vitae)
Abstract: A navigation mesh (or Navmesh) is a data structure that allows characters to plan and traverse paths through 2D and 3D environments. Most Navmesh generators create a separate Navmesh for each fixed agent radius by subtracting that radius from the traversable space. Methods that generate a unified Navmesh are currently too limited to be used in a standard game development workflow. In this thesis, we work towards letting a state-of-the-art Navmesh (Recast) support arbitrary agent radii. For this, we have defined a new concept, Local Clearance Minimum (LCM), a specific line segment that allows us to unify all Navmeshes into one. We show how to extract all LCMs of an environment in Recast using a new watershed implementation. We show that the performance of this new watershed algorithm is on par with Recast’s built-in watershed algorithm. We also show that the algorithm can extract most LCMs accurately, but there are some limitations due to the current (voxel-based) Recast pipeline. Because these issues can be resolved in future work, we argue that LCMs are a promising practical concept for enabling any-radius pathfinding on a single Navmesh.
In this contest, we had to be in a team of three. You got five problems, and you had to solve them using programming. The twist is that only one screen, keyboard and mouse were allowed. Only one person could do the programming. The other two had to figure out how to solve the problem and tell the typing programmer what to do. The goal was to follow a white line with a small autonomous car via one or more optical sensor(s).

Because of some mechanical problems, we had to rewrite the entire program one minute before the start of the competition.
I had an internship at Unity Technologies back in Q1 and Q2 of 2022. I was part of the texture API team.