Introduction to algorithms
A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the parameters of an illumination model using photometric stereo
Graphical Models and Image Processing
Extracting the Shape and Roughness of Specular Lobe Objects Using Four Light Photometric Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Robot Vision
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Computer Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Image-based Rendering with Controllable Illumination
Proceedings of the Eurographics Workshop on Rendering Techniques '97
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Real-time tracking of image regions with changes in geometry and illumination
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
All-frequency shadows using non-linear wavelet lighting approximation
ACM SIGGRAPH 2003 Papers
Modeling Geometric Structure and Illumination Variation of a Scene from Real Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Stereo Reconstruction from Multiperspective Panoramas
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
TensorTextures: multilinear image-based rendering
ACM SIGGRAPH 2004 Papers
Dense Photometric Stereo Using Tensorial Belief Propagation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dense Photometric Stereo Using a Mirror Sphere and Graph Cut
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Performance relighting and reflectance transformation with time-multiplexed illumination
ACM SIGGRAPH 2005 Papers
Shape and Spatially-Varying BRDFs from Photometric Stereo
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shapelets Correlated with Surface Normals Produce Surfaces
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Shape and materials by example: a photometric stereo approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
The plenoptic illumination function
IEEE Transactions on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
An RBF-based compression method for image-based relighting
IEEE Transactions on Image Processing
Compressing the illumination-adjustable images with principal component analysis
IEEE Transactions on Circuits and Systems for Video Technology
ShapePalettes: interactive normal transfer via sketching
ACM SIGGRAPH 2007 papers
Two-dimensional BRDF estimation from polarisation
Computer Vision and Image Understanding
Image based reconstruction using hybrid optimization of simulated annealing and genetic algorithm
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Iterated conditional modes for inverse dithering
Signal Processing
Median Photometric Stereo as Applied to the Segonko Tumulus and Museum Objects
International Journal of Computer Vision
Visibility subspaces: uncalibrated photometric stereo with shadows
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Photometric stereo from maximum feasible Lambertian reflections
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Hi-index | 0.14 |
We address the problem of robust normal reconstruction by dense photometric stereo, in the presence of complex geometry, shadows, highlight, transparencies, variable attenuation in light intensities, and inaccurate estimation in light directions. The input is a dense set of noisy photometric images, conveniently captured by using a very simple set-up consisting of a digital video camera, a reflective mirror sphere, and a handheld spotlight. We formulate the dense photometric stereo problem as a Markov network and investigate two important inference algorithms for Markov Random Fields (MRFs)—graph cuts and belief propagation—to optimize for the most likely setting for each node in the network. In the graph cut algorithm, the MRF formulation is translated into one of energy minimization. A discontinuity-preserving metric is introduced as the compatibility function, which allows \alpha-expansion to efficiently perform the maximum a posteriori (MAP) estimation. Using the identical dense input and the same MRF formulation, our tensor belief propagation algorithm recovers faithful normal directions, preserves underlying discontinuities, improves the normal estimation from one of discrete to continuous, and drastically reduces the storage requirement and running time. Both algorithms produce comparable and very faithful normals for complex scenes. Although the discontinuity-preserving metric in graph cuts permits efficient inference of optimal discrete labels with a theoretical guarantee, our estimation algorithm using tensor belief propagation converges to comparable results, but runs faster because very compact messages are passed and combined. We present very encouraging results on normal reconstruction. A simple algorithm is proposed to reconstruct a surface from a normal map recovered by our method. With the reconstructed surface, an inverse process, known as relighting in computer graphics, is proposed to synthesize novel images of the given scene under user-specified light source and direction. The synthesis is made to run in real time by exploiting the state-of-the-art graphics processing unit (GPU). Our method offers many unique advantages over previous relighting methods and can handle a wide range of novel light sources and directions.