| // Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. | |
| // All rights reserved. | |
| // | |
| // Redistribution and use in source and binary forms, with or without | |
| // modification, are permitted provided that the following conditions are met: | |
| // | |
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| // | |
| // * Redistributions in binary form must reproduce the above copyright | |
| // notice, this list of conditions and the following disclaimer in the | |
| // documentation and/or other materials provided with the distribution. | |
| // | |
| // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of | |
| // its contributors may be used to endorse or promote products derived | |
| // from this software without specific prior written permission. | |
| // | |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
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| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| namespace mvs { | |
| struct PoissonMeshingOptions { | |
| // This floating point value specifies the importance that interpolation of | |
| // the point samples is given in the formulation of the screened Poisson | |
| // equation. The results of the original (unscreened) Poisson Reconstruction | |
| // can be obtained by setting this value to 0. | |
| double point_weight = 1.0; | |
| // This integer is the maximum depth of the tree that will be used for surface | |
| // reconstruction. Running at depth d corresponds to solving on a voxel grid | |
| // whose resolution is no larger than 2^d x 2^d x 2^d. Note that since the | |
| // reconstructor adapts the octree to the sampling density, the specified | |
| // reconstruction depth is only an upper bound. | |
| int depth = 13; | |
| // If specified, the reconstruction code assumes that the input is equipped | |
| // with colors and will extrapolate the color values to the vertices of the | |
| // reconstructed mesh. The floating point value specifies the relative | |
| // importance of finer color estimates over lower ones. | |
| double color = 32.0; | |
| // This floating point values specifies the value for mesh trimming. The | |
| // subset of the mesh with signal value less than the trim value is discarded. | |
| double trim = 10.0; | |
| // The number of threads used for the Poisson reconstruction. | |
| int num_threads = -1; | |
| bool Check() const; | |
| }; | |
| struct DelaunayMeshingOptions { | |
| // Unify input points into one cell in the Delaunay triangulation that fall | |
| // within a reprojected radius of the given pixels. | |
| double max_proj_dist = 20.0; | |
| // Maximum relative depth difference between input point and a vertex of an | |
| // existing cell in the Delaunay triangulation, otherwise a new vertex is | |
| // created in the triangulation. | |
| double max_depth_dist = 0.05; | |
| // The standard deviation of wrt. the number of images seen by each point. | |
| // Increasing this value decreases the influence of points seen in few images. | |
| double visibility_sigma = 3.0; | |
| // The factor that is applied to the computed distance sigma, which is | |
| // automatically computed as the 25th percentile of edge lengths. A higher | |
| // value will increase the smoothness of the surface. | |
| double distance_sigma_factor = 1.0; | |
| // A higher quality regularization leads to a smoother surface. | |
| double quality_regularization = 1.0; | |
| // Filtering thresholds for outlier surface mesh faces. If the longest side of | |
| // a mesh face (longest out of 3) exceeds the side lengths of all faces at a | |
| // certain percentile by the given factor, then it is considered an outlier | |
| // mesh face and discarded. | |
| double max_side_length_factor = 25.0; | |
| double max_side_length_percentile = 95.0; | |
| // The number of threads to use for reconstruction. Default is all threads. | |
| int num_threads = -1; | |
| bool Check() const; | |
| }; | |
| // Perform Poisson surface reconstruction and return true if successful. | |
| bool PoissonMeshing(const PoissonMeshingOptions& options, | |
| const std::string& input_path, | |
| const std::string& output_path); | |
| // Delaunay meshing of sparse and dense COLMAP reconstructions. This is an | |
| // implementation of the approach described in: | |
| // | |
| // P. Labatut, J‐P. Pons, and R. Keriven. "Robust and efficient surface | |
| // reconstruction from range data". Computer graphics forum, 2009. | |
| // | |
| // In case of sparse input, the path should point to a sparse COLMAP | |
| // reconstruction. In case of dense input, the path should point to a dense | |
| // COLMAP workspace folder, which has been fully processed by the stereo and | |
| // fusion pipeline. | |
| void SparseDelaunayMeshing(const DelaunayMeshingOptions& options, | |
| const std::string& input_path, | |
| const std::string& output_path); | |
| void DenseDelaunayMeshing(const DelaunayMeshingOptions& options, | |
| const std::string& input_path, | |
| const std::string& output_path); | |
| } // namespace mvs | |
| } // namespace colmap | |