mirror of https://github.com/CGAL/cgal
Make estimate_variances static and public
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@ -84,7 +84,7 @@ public:
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: GarlandHeckbert_probabilistic_policies(tmesh, dm, 1.0, 1.0) // the one is dummy value
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{
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// set the actual estimate variances
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estimate_variance(tmesh);
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std::tie(sdev_n_2, sdev_p_2) = estimate_variances(tmesh);
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}
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GarlandHeckbert_probabilistic_policies(TriangleMesh& tmesh,
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@ -111,7 +111,6 @@ public:
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opt_pt = X.inverse().col(3); // == X.inverse() * (0 0 0 1)
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return opt_pt;
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}
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Mat_4 construct_quadric_from_normal(const Vector_3& mean_normal, const Point_3& point,
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@ -171,7 +170,7 @@ public:
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Vertex_cost_map vcm_;
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// give a very rough estimate of a decent variance for both parameters
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void estimate_variance(const TriangleMesh& mesh)
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static std::pair<FT, FT> estimate_variances(const TriangleMesh& mesh)
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{
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typedef typename TriangleMesh::Vertex_index vertex_descriptor;
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// get the bounding box of the mesh
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@ -193,8 +192,9 @@ public:
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// set the variances based on the default value for [-1, 1]^3 cubes
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// here we have the maximum edge length, so if we scale
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// our mesh down by max_edge_length it fits inside a [0, 1]^3 cube, hence the "/ 2"
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sdev_n_2 = good_default_variance_unit * max_edge_length / 2.0;
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sdev_p_2 = good_default_variance_unit * max_edge_length / 2.0;
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const FT n2 = good_default_variance_unit * max_edge_length / 2.0;
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const FT p2 = good_default_variance_unit * max_edge_length / 2.0;
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return std::make_pair(n2, p2);
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}
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// magic number determined by some testing, this is a good variance for models that
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