Lukas Radl

Passionate about digitally representing the physical world

Currently in Zurich, CH

I'm a University Assistant/PhD Student at the Institute of Visual Computing, Graz University of Technology. I work on 3D Scene Representations for View Synthesis, supervised by Markus Steinberger.

Previously, I received my Master's degree in Computer Science (with distinction) and my Bachelor's degree in Software Engineering both at Graz University of Technology between 2018 and 2023. During this time, I worked as a study assistant, supporting various visual computing classes. In 2019 & 2020, I was featured on the Dean's List of my faculty (top 5% of students).

📰 News

05/25: I'm joining Meta Reality Labs as a Research Scientist Intern!
02/25: VRSplat has been accepted to I3D'25.
02/25: Invited Presentation at CVWW'25!
01/25: I'm attending Wintergraph'25 in Kaprun, Austria!
07/24: I'll be in Denver for HPG'24 and SIGGRAPH'24 to present StopThePop!
06/24: I'm attending CVPR'24 in Seattle to present NeRF Internals and LAENeRF!
05/24: StopThePop has been accepted to SIGGRAPH'24 (Journal Track) and will appear in ACM TOG 43(4).
04/24: Our NeRF Internals paper has been accepted to the CVPR'24 Workshop on Neural Rendering Intelligence!
02/24: LAENeRF has been accepted to CVPR'24!
02/24: Starting my PhD at Graz University of Technology!

🔬 Research

My research lies at the intersection of 3D, Computer Graphics, Computer Vision, Machine Learning, and Parallel Processing: My recent work focuses on Radiance Field Representations such as Neural Radiance Fields (NeRF) and Gaussian Splatting, aiming to make them more practical for real-time rendering and interactive applications.

	Image-Based Spatio-Temporal Interpolation for Split Rendering Michael Steiner, Thomas Köhler, Lukas Radl, Brian Budge. Markus Steinberger. Computer Graphics Forum, 2025. We investigate a bidirectional reprojection method for challenging split rendering scenarios, and add a dynamic shadow encoding scheme and use auxiliary cameras to handle occlusions.
	AAA-Gaussians: Anti-Aliased and Artifact-Free 3D Gaussian Rendering Michael Steiner, Thomas Köhler, Lukas Radl, Felix Windisch, Dieter Schmalstieg, Markus Steinberger. ICCV, 2025 (Highlight). We introduce a method for Anti-Aliased and Artifact-Free (AAA) Gaussian Rendering, building on per-pixel resorting, view-adaptive filtering and view-space culling.
	VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality Xuechang Tu, Lukas Radl, Michael Steiner, Markus Steinberger. Bernhard Kerbl, Fernando de la Torre. PACMCGIT, 2025. We combine StopThePop, Optimal Projection and Mini-Splatting with single-pass foveated rendering for view-consistent real-time rendering of Gaussian Splats on HMDs.
	Frustum Volume Caching for Accelerated NeRF Rendering Michael Steiner, Thomas Köhler Lukas Radl, Markus Steinberger. PACMCGIT, 2024. We leverage the architecture of Instant-NGP to cache view-independent latent codes in a frustum voxel grid for high-quality real-time and offline rendering.
	StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, Markus Steinberger. SIGGRAPH, 2024 (Journal Track). Sorting 3D Gaussians along view rays leads to increased view-consistency. With our hierarchical, GPU-friendly resorting scheme, our renderer is only 4% slower than 3DGS on average.
	LAENeRF: Local Appearance Editing for Neural Radiance Fields Lukas Radl, Michael Steiner, Andreas Kurz, Markus Steinberger. CVPR, 2024. Locally Stylized Neural Radiance Fields via point-based 3D style transfer with geometry-aware losses - reduced background artefacts, more detail retention and view-consistency.
	Analyzing the Internals of Neural Radiance Fields Lukas Radl, Andreas Kurz, Michael Steiner, Markus Steinberger. CVPR Workshop on Neural Rendering Intelligence, 2024. Using density estimates derived from activations for inverse transform sampling in NeRFs allows for faster inference and comparable visual quality.
	AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks Lukas Radl, Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Jan Egger. Data in Brief, 2022. Method and apparatus for aorta segmentation in Slicer3D, as well as an open-source CTA dataset with ground-truth annotations, to be used in research.

📚 Reviewing

Since 2024, I’ve had the privilege of serving as a reviewer for leading journals and conferences in computer graphics and vision:

✅ Computers & Graphics
✅ Computer Graphics Forum (CGF)
✅ IEEE VR
✅ Eurographics
✅ ICCV
✅ Transactions on Visualization and Computer Graphics

🎓 Teaching

I’ve been actively involved in teaching a range of visual computing courses.
☀️ = Summer term, ❄️ = Winter term.

Tutoring

Worked as a study assistant: guiding students in exercises, grading, and interviews.

2022/23

✅ Real-Time Graphics: Exercise ❄️
✅ Mathematical Principles in Visual Computing: Exercise ☀️
✅ Computer Graphics: Exercise ☀️
✅ Computer Vision: Exercise ☀️

2021/22

✅ Real-Time Graphics: Exercise ❄️
✅ Computer Graphics: Exercise ☀️
✅ Computer Vision: Exercise ☀️

2020/21

✅ Computer Graphics: Exercise ☀️
✅ Computer Vision: Exercise ☀️

Exercise Coordination

Since starting my PhD, I’ve been coordinating exercises for visual computing courses.

2025/26

✅ Real-Time Graphics: Exercise ❄️
✅ Object-Oriented Programming 1: Exercise ❄️
✅ Writing Scientific Papers: Seminar ❄️
✅ Computer Graphics: Exercise ☀️

2024/25

✅ Real-Time Graphics: Exercise ❄️
✅ Object-Oriented Programming 1: Exercise ❄️
✅ Writing Scientific Papers: Seminar ❄️
✅ Computer Graphics: Exercise ☀️

2023/24

✅ Real-Time Graphics: Exercise ❄️
✅ Computer Graphics: Exercise ☀️

🚀 Open Student Projects

Our research group is passionate about real-time rendering, with a special focus on Radiance Fields. Here are some exciting projects you can get involved in:

📌 Currently Open Projects

✅ Advancing 3D Gaussian Splatting
✅ StopThePop in gsplat
⚙️ Real-Time Rendering of Gaussian Splats with Vulkan (in progress)

Interested in joining? Reach out to me (preferably via email) 📧

Template adapted from Jon Barron.