Name: Norman Müller
Position: Ph.D Candidate
Phone: +49-89-289-19595
Room No: 02.07.034


I am PhD Student in the Visual Computing Lab advised by Prof. Matthias Nießner. I received my Bachelor’s and Master’s degree in Computer Science as well as in Mathematics at RWTH Aachen University. In my current work, I research tracking, segmentation, and completion in dynamic 3D scenes.



GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Barbara Rössle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Matthias Nießner
SIGGRAPH Asia 2023
GANeRF proposes an adversarial formulation whose gradients provide feedback for a 3D-consistent neural radiance field representation. This introduces additional constraints that enable more realistic novel view synthesis.
[video][bibtex][project page]

Panoptic Lifting for 3D Scene Understanding with Neural Fields
Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Norman Müller, Matthias Nießner, Angela Dai, Peter Kontschieder
CVPR 2023
Given only RGB images of an in-the-wild scene as input, Panoptic Lifting optimizes a panoptic radiance field which can be queried for color, depth, semantics, and instances for any point in space.
[bibtex][project page]

DiffRF: Rendering-guided 3D Radiance Field Diffusion
Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, Matthias Nießner
CVPR 2023
DiffRF is a denoising diffusion probabilistic model directly operating on 3D radiance fields and trained with an additional volumetric rendering loss. This enables learning strong radiance priors with high rendering quality and accurate geometry. This appraoch naturally enables tasks like 3D masked completion or image-to-volume synthesis.
[video][bibtex][project page]


AutoRF: Learning 3D Object Radiance Fields from Single View Observations
Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, Peter Kontschieder
CVPR 2022
From just a single view, we learn neural 3D object representations for free novel view synthesis. This setting is in stark contrast to the majority of existing works that leverage multiple views of the same object, employ explicit priors during training, or require pixel-perfect annotations. Our method decouples object geometry, appearance, and pose enabling generalization to unseen objects, even across different datasets of challenging real-world street scenes such as nuScenes, KITTI, and Mapillary Metropolis.
[video][bibtex][project page]


Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences
Norman Müller, Yu-Shiang Wong, Niloy J. Mitra, Angela Dai, Matthias Nießner
CVPR 2021
We introduce a novel method to jointly predict complete geometry and dense correspondences of rigidly moving objects for 3D multi-object tracking on RGB-D sequences. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance.
[video][bibtex][project page]