Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Voronoi-based variational reconstruction of unoriented point sets
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Cone carving for surface reconstruction
ACM SIGGRAPH Asia 2010 papers
Fusion of depth maps with multiple scales
Proceedings of the 2011 SIGGRAPH Asia Conference
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Robust surface reconstruction from sample points is a challenging problem, especially for real-world input data. We present a new hierarchical surface reconstruction based on volumetric graph-cuts that incorporates significant improvements over existing methods. One key aspect of our method is, that we exploit the footprint information which is inherent to each sample point and describes the underlying surface region represented by that sample. We interpret each sample as a vote for a region in space where the size of the region depends on the footprint size. In our method, sample points with large footprints do not destroy the fine detail captured by sample points with small footprints. The footprints also steer the inhomogeneous volumetric resolution used locally in order to capture fine detail even in large-scale scenes. Similar to other methods our algorithm initially creates a crust around the unknown surface. We propose a crust computation capable of handling data from objects that were only partially sampled, a common case for data generated by multi-view stereo algorithms. Finally, we show the effectiveness of our method on challenging outdoor data sets with samples spanning orders of magnitude in scale.