Pinned Repositories
DepthSRfromShading
This code implements the approach for the following research paper: Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading; B. Haefner, Y. Quéau, T. Möllenhoff, D. Cremers; Computer Vision and Pattern Recognition (CVPR), 2018.
general_ups_python
Python implementation for Variational Uncalibrated Photometric Stereo under General Lighting (Haefner, B., Ye, Z., Gao, M., Wu, T., Quéau, Y. and Cremers, D.), In International Conference on Computer Vision (ICCV), 2019. Resources
lscm
This code uses geogram to compute least squares conformal maps based on the paper "Least Squares Conformal Maps for Automatic Texture Atlas Generation" Bruno Lévy, Sylvain Petitjean, Nicolas Ray, and Jérome Maillot, TOG 2002
minimal_surface
This code implements the following research: Fast and Globally Optimal Single View Reconstruction of Curved Objects (M. R. Oswald, E. Toeppe and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
ml-neilfpp
MumfordShahCuda
This repository contains an implementation based on the paper: Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional, E. Strekalovskiy, D. Cremers, European Conference on Computer Vision (ECCV), 2014
mweCmakeMexCppCuda
Minimal working example (MWE) on how to use CMake, CUDA, MEX, C++ and library support at once
nerf-pytorch
A PyTorch re-implementation of Neural Radiance Fields
SRmeetsPS
Source code for the paper "Depth Super-Resolution Meets Uncalibrated Photometric Stereo"
SRmeetsPS-CUDA
CUDA implementation of the paper "Depth Super-Resolution Meets Uncalibrated Photometric Stereo"
BjoernHaefner's Repositories
BjoernHaefner/DepthSRfromShading
This code implements the approach for the following research paper: Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading; B. Haefner, Y. Quéau, T. Möllenhoff, D. Cremers; Computer Vision and Pattern Recognition (CVPR), 2018.
BjoernHaefner/mweCmakeMexCppCuda
Minimal working example (MWE) on how to use CMake, CUDA, MEX, C++ and library support at once
BjoernHaefner/general_ups_python
Python implementation for Variational Uncalibrated Photometric Stereo under General Lighting (Haefner, B., Ye, Z., Gao, M., Wu, T., Quéau, Y. and Cremers, D.), In International Conference on Computer Vision (ICCV), 2019. Resources
BjoernHaefner/SRmeetsPS-CUDA
CUDA implementation of the paper "Depth Super-Resolution Meets Uncalibrated Photometric Stereo"
BjoernHaefner/SRmeetsPS
Source code for the paper "Depth Super-Resolution Meets Uncalibrated Photometric Stereo"
BjoernHaefner/MumfordShahCuda
This repository contains an implementation based on the paper: Real-Time Minimization of the Piecewise Smooth Mumford-Shah Functional, E. Strekalovskiy, D. Cremers, European Conference on Computer Vision (ECCV), 2014
BjoernHaefner/lscm
This code uses geogram to compute least squares conformal maps based on the paper "Least Squares Conformal Maps for Automatic Texture Atlas Generation" Bruno Lévy, Sylvain Petitjean, Nicolas Ray, and Jérome Maillot, TOG 2002
BjoernHaefner/minimal_surface
This code implements the following research: Fast and Globally Optimal Single View Reconstruction of Curved Objects (M. R. Oswald, E. Toeppe and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
BjoernHaefner/ml-neilfpp
BjoernHaefner/nerf-pytorch
A PyTorch re-implementation of Neural Radiance Fields
BjoernHaefner/tum-thesis-latex
:notebook_with_decorative_cover: A LaTeX template for TUM Bachelor/Master theses.
BjoernHaefner/neuralangelo
Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)
BjoernHaefner/psnerf
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo (ECCV 2022)
BjoernHaefner/WildLight
official implementation of our CVPR 2023 paper "In-the-wild Inverse Rendering with a Flashlight"