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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

neuralangelo

Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)

psnerf

PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo (ECCV 2022)

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"

tum-thesis-latex

:notebook_with_decorative_cover: A LaTeX template for TUM Bachelor/Master theses.

wildlight

official implementation of our CVPR 2023 paper "In-the-wild Inverse Rendering with a Flashlight"