Photometric method for determining surface orientation from multiple images
Shape from shading
Height and gradient from shading
International Journal of Computer Vision
Instance-Based Learning Algorithms
Machine Learning
Estimation of Illuminant Direction, Albedo, and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Shape-from-Shading by Radial Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Radiometry of Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Enhanced 3D Shape Recovery Using the Neural-Based Hybrid Reflectance Model
Neural Computation
Learning shape from shading by a multilayer network
IEEE Transactions on Neural Networks
A neural network scheme for transparent surface modelling
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Integrative 3D modelling of complex carving surface
Computer-Aided Design
A neural network for simultaneously reconstructing transparent and opaque surfaces
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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We introduce a multiple-view 3D-shape-reconstruction system. This system is able to fuse few-view and erroneous depth maps into a more complete and more accurate shape representation using a unique neural network (NN). The NN provides analytic mapping and learning of a polyhedron model to approximate the true shape of an object based on multiple-view depth maps. The depth maps are obtained by a widely used Tsai-Shah shape-from-shading (SFS) algorithm. They are considered as partial 3D shapes of the object to be reconstructed. The main insight of this work is that the NN minimizes the depth map error in one view using depth maps information from other views observed under nonfixed light source positions relative to the object. Theoretically, we formulate our problem as nonparametric (local) regression in depth space formed by multiple view observations. Experimentally, we obtain exact and stable results through hierarchical reconstruction and annealing reinforcement. We provide the implementation of the NN used in this paper at .