20 citations · 33 across the 6 of their papers we have counts for
9 papers
LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes
Ruofan Liang, Norman Müller, Ethan Weber +4
We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative image-based light decomposition…
Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation
Andrea Simonelli, Norman Müller, Peter Kontschieder
The increasing availability of digital 3D environments, whether through image-based 3D reconstruction, generation, or scans obtained by robots, is driving innovation across various…
Generative Gaussian Splatting: Generating 3D Scenes with Video Diffusion Priors
Katja Schwarz, Norman Mueller, Peter Kontschieder
Synthesizing consistent and photorealistic 3D scenes is an open problem in computer vision. Video diffusion models generate impressive videos but cannot directly synthesize 3D repr…
Coherent 3D Scene Diffusion From a Single RGB Image
Manuel Dahnert, Angela Dai, Norman Müller +1
We present a novel diffusion-based approach for coherent 3D scene reconstruction from a single RGB image. Our method utilizes an image-conditioned 3D scene diffusion model to simul…
L3DG: Latent 3D Gaussian Diffusion
Barbara Roessle, Norman Müller, Lorenzo Porzi +4
We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, sc…
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Barbara Roessle, Norman Müller, Lorenzo Porzi +3
Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to…
3D Multi-Object Tracking with Differentiable Pose Estimation
Dominik Schmauser, Zeju Qiu, Norman Müller +1
We propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments. To this end, we detect and reconstruct objects in eac…
AutoRF: Learning 3D Object Radiance Fields from Single View Observations
Norman Müller, Andrea Simonelli, Lorenzo Porzi +3
We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view. This setting is in stark…
Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences
Norman Müller, Yu-Shiang Wong, Niloy J. Mitra +2
Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having th…