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Hoip10 presentación reconstrucción de superficies_upc
 

Hoip10 presentación reconstrucción de superficies_upc

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Presentación de la Universidad Politécnica de Catalunya sobre reconstrucciónde superficies realizada durante las jornadas HOIP 2010 organizadas por la Unidad de Sistemas de Información e ...

Presentación de la Universidad Politécnica de Catalunya sobre reconstrucciónde superficies realizada durante las jornadas HOIP 2010 organizadas por la Unidad de Sistemas de Información e Interacción TECNALIA.

Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm

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    Hoip10 presentación reconstrucción de superficies_upc Hoip10 presentación reconstrucción de superficies_upc Presentation Transcript

    • Motivation and SoA Propagation Algorithm Experimental Results Conclusion Surface Reconstruction byRestricted and Oriented PropagationXavier Suau Josep R. Casas Javier Ruiz-Hidalgo {xavier.suau, josep.ramon.casas, j.ruiz}@upc.edu Universitat Politècnica de Catalunya November 16, 2010
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionOutline 1 Motivation and state of the art 2 Propagation Algorithm 3 Experimental Results 4 Conclusion Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 1 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionContext Large 3D point clouds are very common datasets, being mostly obtained from: Laser scans Multiview datasets Virtual datasets The objective is to have a meshed representation of these type of datasets in this case, for visualization purposes in a fast, up to real-time, way Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 2 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionState of the Art Results are evaluated against a reference composed of:Ball-Pivoting Algorithm Poisson Reconstruction Marching Cubes + APSS • Very accurate • Watertight reconstructed • Watertight reconstructed reconstruction surface surface • Sensitive to density • Fast reconstructions provide • Voxelization required variations low level of detail all of them implemented in the MeshLab software c Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 3 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionState of the Art Results are evaluated against a reference composed of:Ball-Pivoting Algorithm Poisson Reconstruction Marching Cubes + APSS • Very accurate • Watertight reconstructed • Watertight reconstructed reconstruction surface surface • Sensitive to density • Fast reconstructions provide • Voxelization required variations low level of detail all of them implemented in the MeshLab software c Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 3 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionState of the Art Results are evaluated against a reference composed of:Ball-Pivoting Algorithm Poisson Reconstruction Marching Cubes + APSS • Very accurate • Watertight reconstructed • Watertight reconstructed reconstruction surface surface • Sensitive to density • Fast reconstructions provide • Voxelization required variations low level of detail all of them implemented in the MeshLab software c Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 3 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionState of the Art Results are evaluated against a reference composed of:Ball-Pivoting Algorithm Poisson Reconstruction Marching Cubes + APSS • Very accurate • Watertight reconstructed • Watertight reconstructed reconstruction surface surface • Sensitive to density • Fast reconstructions provide • Voxelization required variations low level of detail all of them implemented in the MeshLab software c Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 3 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionOutline 1 Motivation and state of the art 2 Propagation Algorithm 3 Experimental Results 4 Conclusion Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 4 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionAlgorithm overview From 3D point clouds... ...to meshed surfaces Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 5 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionAlgorithm overview From 3D point clouds... ...to meshed surfaces Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 5 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionAlgorithm overview From 3D point clouds... ...to meshed surfaces Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 5 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionVoxelization • The target point cloud S is composed of points pi = (Pi , Ci ) with Pi = (xi , yi , zi ) and Ci = (ri , gi , bi ) • Voxels υk are associated to pi as follows 0 points in voxel 1 point p = (P, C) in voxel m points pj υk ← ∅ υk ← (P, C) υk ← (P, C) Voxels υk = ∅ are called seed voxels, or υS Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 6 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionVoxelization • The target point cloud S is composed of points pi = (Pi , Ci ) with Pi = (xi , yi , zi ) and Ci = (ri , gi , bi ) • Voxels υk are associated to pi as follows 0 points in voxel 1 point p = (P, C) in voxel m points pj υk ← ∅ υk ← (P, C) υk ← (P, C) Voxels υk = ∅ are called seed voxels, or υS Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 6 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Pattern Propagation, why? To nd close neighbors in the discretized space How? With a propagation pattern or set of positions relative to a seed voxel Omni-26 Omni-18 Omni-6 6DO Oriented Pattern Knowing that direction of neighbor nding is indierent Omni patterns check both directions, redundant! The 6DO Oriented Pattern • Reduces the amount of redundant edges • Is faster than Omni-18 with the same spatial coverage Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 7 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Pattern Propagation, why? To nd close neighbors in the discretized space How? With a propagation pattern or set of positions relative to a seed voxel Omni-26 Omni-18 Omni-6 6DO Oriented Pattern Knowing that direction of neighbor nding is indierent Omni patterns check both directions, redundant! The 6DO Oriented Pattern • Reduces the amount of redundant edges • Is faster than Omni-18 with the same spatial coverage Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 7 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Pattern Propagation, why? To nd close neighbors in the discretized space How? With a propagation pattern or set of positions relative to a seed voxel Omni-26 Omni-18 Omni-6 6DO Oriented Pattern Knowing that direction of neighbor nding is indierent Omni patterns check both directions, redundant! The 6DO Oriented Pattern • Reduces the amount of redundant edges • Is faster than Omni-18 with the same spatial coverage Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 7 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Pattern Propagation, why? To nd close neighbors in the discretized space How? With a propagation pattern or set of positions relative to a seed voxel Omni-26 Omni-18 Omni-6 6DO Oriented Pattern Knowing that direction of neighbor nding is indierent Omni patterns check both directions, redundant! The 6DO Oriented Pattern • Reduces the amount of redundant edges • Is faster than Omni-18 with the same spatial coverage Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 7 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionPropagation Steps Iterative Algorithm • Propagation starts at every seed voxel υiS • Voxels ∈ 6DO are associated to its seed voxels υiS , building up seed volumes Vi that grow at every iteration • At propagation end, intersections Vi ∩ Vj dene pairs of neighbors pi pj • Triangular faces are obtained from the list of neighbors Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 8 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionStop Threshold Propagation iterations should be stopped at the appropriate moment to avoid meshing distant points Edge Density • The number of created edges per iteration is called edge density or De • D e presents a rst maximum D e max at a low number of iterations κmax , which corresponds to the meshing of the main surface • Propagation stops at iteration k which veries: 1 κ ≥ 2κmax ) ∧ e (κ) < 4 e D D max Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 9 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionStop Threshold Propagation iterations should be stopped at the appropriate moment to avoid meshing distant points Edge Density • The number of created edges per iteration is called edge density or De • D e presents a rst maximum D e max at a low number of iterations κmax , which corresponds to the meshing of the main surface • Propagation stops at iteration k which veries: 1 κ ≥ 2κmax ) ∧ e (κ) < 4 e D D max Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 9 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionStop Threshold Propagation iterations should be stopped at the appropriate moment to avoid meshing distant points Edge Density • The number of created edges per iteration is called edge density or De • D e presents a rst maximum D e max at a low number of iterations κmax , which corresponds to the meshing of the main surface • Propagation stops at iteration k which veries: 1 κ ≥ 2κmax ) ∧ e (κ) < 4 e D D max Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 9 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionStop Threshold Propagation iterations should be stopped at the appropriate moment to avoid meshing distant points Edge Density • The number of created edges per iteration is called edge density or De • D e presents a rst maximum D e max at a low number of iterations κmax , which corresponds to the meshing of the main surface • Propagation stops at iteration k which veries: 1 κ ≥ 2κmax ) ∧ e (κ) < 4 e D D max Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 9 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionOutline 1 Motivation and state of the art 2 Propagation Algorithm 3 Experimental Results 4 Conclusion Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 10 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation of results Quantitative Evaluation Two main characteristics are evaluated: δH Hausdor Distance metric between a groundtruth surface and a reconstructed surface tO Overall calculation time on a 64-bit Intel Xeon CPU @ 3.00GHz processor (includes memory allocation and mesh writing) Results are presented on an Accuracy Vs. Speed (δH , tO ) plane Qualitative Evaluation Global visual inspection Four 3D models provided by the Stanford 3D Scanning Repository are tested: Bunny Hand Dragon Happy Buddha Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 11 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation of results Quantitative Evaluation Two main characteristics are evaluated: δH Hausdor Distance metric between a groundtruth surface and a reconstructed surface tO Overall calculation time on a 64-bit Intel Xeon CPU @ 3.00GHz processor (includes memory allocation and mesh writing) Results are presented on an Accuracy Vs. Speed (δH , tO ) plane Qualitative Evaluation Global visual inspection Four 3D models provided by the Stanford 3D Scanning Repository are tested: Bunny Hand Dragon Happy Buddha Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 11 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation of results Quantitative Evaluation Two main characteristics are evaluated: δH Hausdor Distance metric between a groundtruth surface and a reconstructed surface tO Overall calculation time on a 64-bit Intel Xeon CPU @ 3.00GHz processor (includes memory allocation and mesh writing) Results are presented on an Accuracy Vs. Speed (δH , tO ) plane Qualitative Evaluation Global visual inspection Four 3D models provided by the Stanford 3D Scanning Repository are tested: Bunny Hand Dragon Happy Buddha Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 11 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation of results Quantitative Evaluation Two main characteristics are evaluated: δH Hausdor Distance metric between a groundtruth surface and a reconstructed surface tO Overall calculation time on a 64-bit Intel Xeon CPU @ 3.00GHz processor (includes memory allocation and mesh writing) Results are presented on an Accuracy Vs. Speed (δH , tO ) plane Qualitative Evaluation Global visual inspection Four 3D models provided by the Stanford 3D Scanning Repository are tested: Bunny Hand Dragon Happy Buddha Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 11 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation of results Quantitative Evaluation Two main characteristics are evaluated: δH Hausdor Distance metric between a groundtruth surface and a reconstructed surface tO Overall calculation time on a 64-bit Intel Xeon CPU @ 3.00GHz processor (includes memory allocation and mesh writing) Results are presented on an Accuracy Vs. Speed (δH , tO ) plane Qualitative Evaluation Global visual inspection Four 3D models provided by the Stanford 3D Scanning Repository are tested: Bunny Hand Dragon Happy Buddha Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 11 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionVoxelization eect Voxelization resolution is ReOPs critical parameter • Low resolution: Poor visual quality • High resolution: Higher calculation time and memory requirements 76×57×34 voxels 226×170×101 voxels 376×283×168 voxels 11,145 vertices 85,082 vertices 181,509 vertices 76,124 faces 529,916 faces 994,578 faces 1.2 s 8.9 s 17.3 s Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 12 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Happy Buddha dataset (543,652 points) (δH , tO ) plane Point Cloud Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 13 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Happy Buddha dataset (543,652 points) (δH , tO ) plane Ball-Pivoting 238, 193 faces (δH , tO ) = (0.000719, 1429 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 13 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Happy Buddha dataset (543,652 points) (δH , tO ) plane MCubes+APSS 2, 641, 481 faces (δH , tO ) = (0.000046, 528 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 13 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Happy Buddha dataset (543,652 points) (δH , tO ) plane Poisson Reconstruction 631, 480 faces (δH , tO ) = (0.000184, 65.1 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 13 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Happy Buddha dataset (543,652 points) (δH , tO ) plane ReOP 1, 367, 336 faces (δH , tO ) = (0.000031, 22.2 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 13 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionComparative (Happy Buddha - 543,652 points)Ball-Pivoting MCubes+APSS Poisson Rec. ReOP 238, 193 faces 2, 641, 481 faces 631, 480faces 1, 367, 336 faces tO ) = (δH , tO ) = (δH , tO ) = (δH , tO ) = (δH , (0.000719, 1429 s ) (0.000046, 528 s ) (0.000184, 65.1 s ) (0.000031, 22.2 s ) Results on Happy Buddha, largest dataset • About 23x faster than MCubes+APSS for a similar good quality • Reasonable amount of faces, about 2.5 · Npoints Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 14 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionComparative (Happy Buddha - 543,652 points)Ball-Pivoting MCubes+APSS Poisson Rec. ReOP 238, 193 faces 2, 641, 481 faces 631, 480faces 1, 367, 336 faces tO ) = (δH , tO ) = (δH , tO ) = (δH , tO ) = (δH , (0.000719, 1429 s ) (0.000046, 528 s ) (0.000184, 65.1 s ) (0.000031, 22.2 s ) Results on Happy Buddha, largest dataset • About 23x faster than MCubes+APSS for a similar good quality • Reasonable amount of faces, about 2.5 · Npoints Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 14 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionComparative (Happy Buddha - 543,652 points)Ball-Pivoting MCubes+APSS Poisson Rec. ReOP 238, 193 faces 2, 641, 481 faces 631, 480faces 1, 367, 336 faces tO ) = (δH , tO ) = (δH , tO ) = (δH , tO ) = (δH , (0.000719, 1429 s ) (0.000046, 528 s ) (0.000184, 65.1 s ) (0.000031, 22.2 s ) Results on Happy Buddha, largest dataset • About 23x faster than MCubes+APSS for a similar good quality • Reasonable amount of faces, about 2.5 · Npoints Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 14 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Stanford Bunny dataset (35,947 points) (δH , tO ) plane Point Cloud Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 15 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Stanford Bunny dataset (35,947 points) (δH , tO ) plane Ball-Pivoting 238, 193 faces (δH , tO ) = (0.000113, 8.2 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 15 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Stanford Bunny dataset (35,947 points) (δH , tO ) plane MCubes+APSS 2, 641, 481 faces (δH , tO ) = (0.000042, 23 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 15 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Stanford Bunny dataset (35,947 points) (δH , tO ) plane Poisson Reconstruction 631, 480 faces (δH , tO ) = (0.000285, 10.3 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 15 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionEvaluation on the (δH , tO ) plane Stanford Bunny dataset (35,947 points) (δH , tO ) plane ReOP 1, 367, 336 faces (δH , tO ) = (0.000044, 0.96 s ) Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 15 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionComparative (Stanford Bunny - 35,947 points)Ball-Pivoting MCubes+APSS Poisson Rec. ReOP 70, 832faces 769, 029faces 70, 438faces 147, 029faces (δH ,tO ) = tO ) = (δH , tO ) = (δH , tO ) = (δH , (0.000113, 8.2 s ) (0.000042, 23 s ) (0.000285, 10.3 s ) (0.000044, 0.96 s ) Results on Stanford Bunny, smallest dataset • About 23x faster than MCubes+APSS for a the same quality • Reasonable amount of faces, about 3 · Npoints Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 16 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionComparative (Stanford Bunny - 35,947 points)Ball-Pivoting MCubes+APSS Poisson Rec. ReOP 70, 832faces 769, 029faces 70, 438faces 147, 029faces (δH ,tO ) = tO ) = (δH , tO ) = (δH , tO ) = (δH , (0.000113, 8.2 s ) (0.000042, 23 s ) (0.000285, 10.3 s ) (0.000044, 0.96 s ) Results on Stanford Bunny, smallest dataset • About 23x faster than MCubes+APSS for a the same quality • Reasonable amount of faces, about 3 · Npoints Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 16 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionComparative (Stanford Bunny - 35,947 points)Ball-Pivoting MCubes+APSS Poisson Rec. ReOP 70, 832faces 769, 029faces 70, 438faces 147, 029faces (δH ,tO ) = tO ) = (δH , tO ) = (δH , tO ) = (δH , (0.000113, 8.2 s ) (0.000042, 23 s ) (0.000285, 10.3 s ) (0.000044, 0.96 s ) Results on Stanford Bunny, smallest dataset • About 23x faster than MCubes+APSS for a the same quality • Reasonable amount of faces, about 3 · Npoints Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 16 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionOutline 1 Motivation and state of the art 2 Propagation Algorithm 3 Experimental Results 4 Conclusion Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 17 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionThe presented ReOP algorithm is... • Surface reconstruction is performed about 23x faster than the reference, for a given quality • ReOP quality is similar to the best reference method • ReOP reconstructed mesh is visually clear and presents few artifacts • The seed voxel/volume structure is suitable to be parallelized on GPU • The output mesh has no manifold propertiesReOP is suitable for... • Real-time applications with small datasets (<50,000 points in experiments) • Large datasets reconstruction (millions of points), such those obtained in multiview applications Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 18 / 20
    • Motivation and SoA Propagation Algorithm Experimental Results ConclusionFuture work • Adapt propagation pattern to topology and sampling density of surfaces • Find faster structures for close neighbor queries (eg. kdtree) • Obtain manifold meshes while preserving execution speed • GPU implementation Xavier Suau, Josep R. Casas, Javier Ruiz-Hidalgo Surface Reconstruction by ReOP 19 / 20
    • Thank YouQuestions