Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Mueller, Norman"'
Autor:
Roessle, Barbara, Müller, Norman, Porzi, Lorenzo, Bulò, Samuel Rota, Kontschieder, Peter, Dai, Angela, Nießner, Matthias
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, scaling to generation of entire room-scale scenes which can be very effi
Externí odkaz:
http://arxiv.org/abs/2410.13530
Autor:
Müller, Norman, Schwarz, Katja, Roessle, Barbara, Porzi, Lorenzo, Bulò, Samuel Rota, Nießner, Matthias, Kontschieder, Peter
We introduce MultiDiff, a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image is highly ill-posed by nature, as there exist multiple, plausible expla
Externí odkaz:
http://arxiv.org/abs/2406.18524
Autor:
Chen, Jun-Kun, Bulò, Samuel Rota, Müller, Norman, Porzi, Lorenzo, Kontschieder, Peter, Wang, Yu-Xiong
This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency, thus enabling high-fidelity instruction-guided scene editing. To overcome the fundamental limitation of missing 3D consistency
Externí odkaz:
http://arxiv.org/abs/2406.09404
Autor:
Höllein, Lukas, Božič, Aljaž, Müller, Norman, Novotny, David, Tseng, Hung-Yu, Richardt, Christian, Zollhöfer, Michael, Nießner, Matthias
3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on
Externí odkaz:
http://arxiv.org/abs/2403.01807
Autor:
Yu, Zhengming, Dou, Zhiyang, Long, Xiaoxiao, Lin, Cheng, Li, Zekun, Liu, Yuan, Müller, Norman, Komura, Taku, Habermann, Marc, Theobalt, Christian, Li, Xin, Wang, Wenping
We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation with different representations and they suffer from limited topologies an
Externí odkaz:
http://arxiv.org/abs/2311.17050
Detecting fire smoke is crucial for the timely identification of early wildfires using satellite imagery. However, the spatial and spectral similarity of fire smoke to other confounding aerosols, such as clouds and haze, often confuse even the most a
Externí odkaz:
http://arxiv.org/abs/2310.01711
Autor:
Roessle, Barbara, Müller, Norman, Porzi, Lorenzo, Bulò, Samuel Rota, Kontschieder, Peter, Nießner, Matthias
Publikováno v:
ACM Transactions on Graphics, Vol. 42, No. 6, Article 207 (2023) 1-14
Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitiga
Externí odkaz:
http://arxiv.org/abs/2306.06044
Autor:
Siddiqui, Yawar, Porzi, Lorenzo, Buló, Samuel Rota, Müller, Norman, Nießner, Matthias, Dai, Angela, Kontschieder, Peter
We propose Panoptic Lifting, a novel approach for learning panoptic 3D volumetric representations from images of in-the-wild scenes. Once trained, our model can render color images together with 3D-consistent panoptic segmentation from novel viewpoin
Externí odkaz:
http://arxiv.org/abs/2212.09802
Autor:
Müller, Norman, Siddiqui, Yawar, Porzi, Lorenzo, Bulò, Samuel Rota, Kontschieder, Peter, Nießner, Matthias
We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models. While existing diffusion-based methods operate on images, latent codes, or point cloud data, we are the first to directly generat
Externí odkaz:
http://arxiv.org/abs/2212.01206
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 each frame while predicting dense correspondences mappings into a normali
Externí odkaz:
http://arxiv.org/abs/2206.13785