1 code implementation • 3 Jun 2024 • Zhipeng Cai, Matthias Mueller, Reiner Birkl, Diana Wofk, Shao-Yen Tseng, Junda Cheng, Gabriela Ben-Melech Stan, Vasudev Lal, Michael Paulitsch
However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e. g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view.
no code implementations • 28 Mar 2024 • Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias Müller, Matthias Nießner
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
2 code implementations • 26 Jul 2023 • Reiner Birkl, Diana Wofk, Matthias Müller
We release MiDaS v3. 1 for monocular depth estimation, offering a variety of new models based on different encoder backbones.
3 code implementations • 23 Feb 2023 • Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller
Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.
Ranked #16 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)