Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
The Euclidean distance transform in arbitrary dimensions
Pattern Recognition Letters
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Information Processing Letters
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Computer Vision and Image Understanding
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Journal of the ACM (JACM)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
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IEEE Transactions on Visualization and Computer Graphics
Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Power Watershed: A Unifying Graph-Based Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building the Component Tree in Quasi-Linear Time
IEEE Transactions on Image Processing
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Surface reconstruction from a set of noisy point measurements has been a well studied problem for several decades. Recently, variational and discrete optimization approaches have been applied to solve it, demonstrating good robustness to outliers thanks to a global energy minimization scheme. In this work, we use a recent approach embedding several optimization algorithms into a common framework named power watershed. We derive a specific watershed algorithm for surface reconstruction which is fast, robust to markers placement, and produces smooth surfaces. Experiments also show that our proposed algorithm compares favorably in terms of speed, memory requirement and accuracy with existing algorithms.