Name: Lukas Höllein
Position: Ph.D Candidate
E-Mail: lukas.hoellein@tum.de
Phone: +49 (89) 289 - 18164
Room No: 02.07.038

Bio

Hi, I am Lukas. I am a PhD student at the Visual Computing Lab with Prof. Matthias Nießner. Before that, I did my Bachelor's in Computer Science at Coburg University and Master's at TU Munich. I worked on "3D Consistent Style Transfer of Indoor Scenes" as my Master's Thesis and on neural rendering of point clouds with the Visual Computing Lab.

Research Interest

Neural Rendering, Novel View Synthesis, Style Transfer, Relighting

Publications

Preprints
3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt
Lukas Höllein, Aljaž Božič, Michael Zollhöfer, Matthias Nießner
arXiv
3DGS-LM accelerates Gaussian-Splatting optimization by replacing the ADAM optimizer with Levenberg-Marquardt. We propose a highly-efficient GPU parallization scheme for PCG and implement it in custom CUDA kernels that compute Jacobian-vector products.
[video][bibtex][project page]
2024
ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models
Lukas Höllein, Aljaž Božič, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, Matthias Nießner
CVPR 2024
ViewDiff generates high-quality, multi-view consistent images of a real-world 3D object in authentic surroundings. We turn pretrained text-to-image model into 3D consistent image generator by finetuning them with multi-view supervision.
[video][bibtex][project page]
2023
Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models
Lukas Höllein, Ang Cao, Andrew Owens, Justin Johnson, Matthias Nießner
ICCV 2023
Text2Room generates textured 3D meshes from a given text prompt using 2D text-to-image models. The core idea of our approach is a tailored viewpoint selection such that the content of each image can be fused into a seamless, textured 3D mesh. More specifically, we propose a continuous alignment strategy that iteratively fuses scene frames with the existing geometry.
[video][bibtex][project page]
2022
StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions
Lukas Höllein, Justin Johnson, Matthias Nießner
CVPR 2022
We apply style transfer on mesh reconstructions of indoor scenes. We optimize an explicit texture for the reconstructed mesh of a scene and stylize it jointly from all available input images. Our depth- and angle-aware optimization leverages surface normal and depth data of the underlying mesh to create a uniform and consistent stylization for the whole scene.
[video][bibtex][project page]