1 code implementation • 28 Nov 2024 • Thomas Wimmer, Michael Oechsle, Michael Niemeyer, Federico Tombari
Our key idea is to leverage powerful video diffusion models as the generative component of our model and to combine these with a robust technique to lift 2D videos into meaningful 3D motion.
no code implementations • 27 May 2024 • Fangneng Zhan, Hanxue Liang, Yifan Wang, Michael Niemeyer, Michael Oechsle, Adam Kortylewski, Cengiz Oztireli, Gordon Wetzstein, Christian Theobalt
Central to this framework is the development of differentiable versions of these rendering elements, allowing for effective gradient backpropagation from the final rendering objectives.
1 code implementation • 26 May 2024 • Erik Sandström, Keisuke Tateno, Michael Oechsle, Michael Niemeyer, Luc van Gool, Martin R. Oswald, Federico Tombari
In response, we propose the first RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes all benefits of globally optimized tracking by adapting dynamically to keyframe pose and depth updates by actively deforming the 3D Gaussian map.
no code implementations • 20 Mar 2024 • Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, Federico Tombari
First, we use radiance fields as a prior and supervision signal for optimizing point-based scene representations, leading to improved quality and more robust optimization.
2 code implementations • ICCV 2021 • Michael Oechsle, Songyou Peng, Andreas Geiger
At the same time, neural radiance fields have revolutionized novel view synthesis.
3 code implementations • 27 Mar 2020 • Michael Oechsle, Michael Niemeyer, Lars Mescheder, Thilo Strauss, Andreas Geiger
In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field.
1 code implementation • CVPR 2020 • Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger
In this work, we propose a differentiable rendering formulation for implicit shape and texture representations.
no code implementations • ICCV 2019 • Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger
A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques.
7 code implementations • CVPR 2019 • Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.