Vulkan · CUDA · Neural Rendering · Simulation
I build renderers, physics engines, and the pipelines that feed them — from hybrid deferred-plus-ray-traced Vulkan to CUDA path tracers with ReSTIR and OIDN, down to the capture and reconstruction infrastructure that turns photons into assets.
MSc Computer Science, Linköping University — Graphics & Visualization. Ships hardware RT pipelines (Vulkan KHR, CUDA), SOTA sampling (ReSTIR, MIS), production denoising (Intel OIDN), differentiable rendering (nvdiffrast + Disney BRDF), and synchronized multi-camera capture systems. Currently physics simulation & rendering intern at Freedo Technology. Open to graphics / engine / GPU-systems roles — available full-time.
Damaged Helmet · deferred PBR · full material stack
Lantern · emissive driving scene lighting
MetalRoughSpheres · BRDF calibration
Path-tracing pipeline · ReSTIR direct illumination + Intel OIDN denoising
ReSTIR OFF · 1 spp · 12 lights · noise dominated
ReSTIR ON · 1 spp · 12 lights · usable signal
Helmet · 16 spp · raw path-traced
Helmet · 16 spp · OIDN denoised
C++20 · Vulkan KHR RT · GLSL · ~50K C++ + 11K GLSL
Standalone Vulkan engine written from scratch. Hybrid renderer: a deferred G-buffer path and a hardware ray-tracing path share one scene representation, bindless resources, and acceleration-structure workflow. Architecture spans render (22K LOC), physics (11K LOC), and a gpu / Vulkan abstraction layer (8K LOC).
Deferred path: Cook-Torrance GGX BRDF, cascaded shadow maps, TAA, SSAO, screen-space reflections. Path-tracing path: Vulkan KHR ray-tracing with multiple-importance sampling, ReSTIR for direct illumination on many-light scenes (1 spp becomes usable instead of unrenderable), and Intel OIDN integration for production-quality output at low sample budgets. Adaptive sampling driven by per-tile variance.
Physics layer: GJK / EPA collision detection over a BVH. Top renders use Khronos glTF Sample Assets — a sanity-check that the engine reproduces the reference results graphics engineers actually verify against.
Gränsö Castle · aerial photogrammetric reconstruction
Method matrix · RC / NeRF / 3DGS · glass, stone, skull
C++ · C# · WinUI · Hardware Control · ~8K lines
Built CamMatrixCapture: a 12-camera synchronized capture system with staggered trigger firmware, Bluetooth-controlled turntable automation, and a state-machine driven capture workflow. Indoor object captures feed NeRF / 3DGS training; outdoor work includes drone reconstruction of Gränsö Castle.
Released a 209 GB public dataset focused on challenging materials — reflective, transparent, translucent surfaces that traditionally break neural rendering methods.
The benchmarking contribution: 3D Gaussian Splatting reaches ~11 dB PSNR above NeRF on reflective surfaces — a surprisingly large gap, with clear implications for production capture pipelines.
CUDA path tracer with BVH, Schlick-Fresnel refraction, and optional Intel OIDN denoising. 16 parameterized liquid presets (ocean · honey · lava · milk · blood · crystal …) each with their own IOR and depth-absorption. Paired with a Taichi-GPU Position-Based Dynamics cloth solver — stretch + bend constraints, material-specific stiffness — exporting per-frame OBJ meshes back to the CUDA renderer. 58 production stills shipped.
Repo ↗
Foundation for a differentiable rendering pipeline that will learn per-point BRDF from turntable captures, then relight glossy objects uniformly so Structure-from-Motion actually converges. Full GGX stack shipped — D (GGX / Beckmann / anisotropic), G (Smith / Schlick-GGX), F (Schlick / roughness-aware) — plus nvdiffrast backend, turntable camera / mesh / lighting layers. Neural decomposition + training loop next. Built on NVIDIA nvdiffrast.
Repo ↗
GPU-accelerated Smoothed Particle Hydrodynamics solver with Marching Cubes surface extraction and CUDA ↔ OpenGL interop for real-time visualization. Uniform-grid spatial hashing for neighbor search, cubic-spline density kernel with gradient / Laplacian variants for pressure and viscosity forces, plus surface-tension kernel. Bachelor thesis — the foundation my current fluid work builds on.
Repo ↗ Demo video ↗I care about infrastructure, not app surface area. The systems I find interesting are the ones other engineers build on top of: a renderer, a capture pipeline, a physics engine, a GPU scheduler. I profile before optimizing, I read the paper before reimplementing, and I'd rather ship one well-understood 37K-line engine than three shallow demos.