Add support for multiple visibility clustering algorithms.
The original visibility based preconditioning paper and
implementation only used the canonical views algorithm.
This algorithm for large dense graphs can be particularly
expensive. As its worst case complexity is cubic in size
of the graph.
Further, for many uses the SCHUR_JACOBI preconditioner
was both effective enough while being cheap. It however
suffers from a fatal flaw. If the camera parameter blocks
are split between two or more parameter blocks, e.g,
extrinsics and intrinsics. The preconditioner because
it is block diagonal will not capture the interactions
between them.
Using CLUSTER_JACOBI or CLUSTER_TRIDIAGONAL will fix
this problem but as mentioned above this can be quite
expensive depending on the problem.
This change extends the visibility based preconditioner
to allow for multiple clustering algorithms. And adds
a simple thresholded single linkage clustering algorithm
which allows you to construct versions of CLUSTER_JACOBI
and CLUSTER_TRIDIAGONAL preconditioners that are cheap
to construct and are more effective than SCHUR_JACOBI.
Currently the constants controlling the threshold above
which edges are considered in the single linkage algorithm
are not exposed. This would be done in a future change.
Change-Id: I7ddc36790943f24b19c7f08b10694ae9a822f5c9
diff --git a/internal/ceres/CMakeLists.txt b/internal/ceres/CMakeLists.txt
index d963fd6..f4a4578 100644
--- a/internal/ceres/CMakeLists.txt
+++ b/internal/ceres/CMakeLists.txt
@@ -91,6 +91,7 @@
schur_eliminator.cc
schur_jacobi_preconditioner.cc
scratch_evaluate_preparer.cc
+ single_linkage_clustering.cc
solver.cc
solver_impl.cc
sparse_matrix.cc
@@ -247,6 +248,7 @@
CERES_TEST(rotation)
CERES_TEST(schur_complement_solver)
CERES_TEST(schur_eliminator)
+ CERES_TEST(single_linkage_clustering)
CERES_TEST(small_blas)
CERES_TEST(solver_impl)
diff --git a/internal/ceres/canonical_views_clustering.cc b/internal/ceres/canonical_views_clustering.cc
index 6531945..044d438 100644
--- a/internal/ceres/canonical_views_clustering.cc
+++ b/internal/ceres/canonical_views_clustering.cc
@@ -57,8 +57,8 @@
// configuration of the clustering algorithm that some of the
// vertices may not be assigned to any cluster. In this case they
// are assigned to a cluster with id = kInvalidClusterId.
- void ComputeClustering(const Graph<int>& graph,
- const CanonicalViewsClusteringOptions& options,
+ void ComputeClustering(const CanonicalViewsClusteringOptions& options,
+ const Graph<int>& graph,
vector<int>* centers,
IntMap* membership);
@@ -81,21 +81,21 @@
};
void ComputeCanonicalViewsClustering(
- const Graph<int>& graph,
const CanonicalViewsClusteringOptions& options,
+ const Graph<int>& graph,
vector<int>* centers,
IntMap* membership) {
time_t start_time = time(NULL);
CanonicalViewsClustering cv;
- cv.ComputeClustering(graph, options, centers, membership);
+ cv.ComputeClustering(options, graph, centers, membership);
VLOG(2) << "Canonical views clustering time (secs): "
<< time(NULL) - start_time;
}
// Implementation of CanonicalViewsClustering
void CanonicalViewsClustering::ComputeClustering(
- const Graph<int>& graph,
const CanonicalViewsClusteringOptions& options,
+ const Graph<int>& graph,
vector<int>* centers,
IntMap* membership) {
options_ = options;
diff --git a/internal/ceres/canonical_views_clustering.h b/internal/ceres/canonical_views_clustering.h
index 48d1ed2..06d80c8 100644
--- a/internal/ceres/canonical_views_clustering.h
+++ b/internal/ceres/canonical_views_clustering.h
@@ -47,9 +47,6 @@
#include "ceres/collections_port.h"
#include "ceres/graph.h"
-#include "ceres/internal/macros.h"
-#include "ceres/map_util.h"
-#include "glog/logging.h"
namespace ceres {
namespace internal {
@@ -100,8 +97,8 @@
// algorithm that some of the vertices may not be assigned to any
// cluster. In this case they are assigned to a cluster with id = -1;
void ComputeCanonicalViewsClustering(
- const Graph<int>& graph,
const CanonicalViewsClusteringOptions& options,
+ const Graph<int>& graph,
vector<int>* centers,
HashMap<int, int>* membership);
diff --git a/internal/ceres/canonical_views_clustering_test.cc b/internal/ceres/canonical_views_clustering_test.cc
index 78d5635..f4f9e2d 100644
--- a/internal/ceres/canonical_views_clustering_test.cc
+++ b/internal/ceres/canonical_views_clustering_test.cc
@@ -69,7 +69,7 @@
}
void ComputeClustering() {
- ComputeCanonicalViewsClustering(graph_, options_, ¢ers_, &membership_);
+ ComputeCanonicalViewsClustering(options_, graph_, ¢ers_, &membership_);
}
Graph<int> graph_;
diff --git a/internal/ceres/iterative_schur_complement_solver.cc b/internal/ceres/iterative_schur_complement_solver.cc
index 90013ff..e9c54c8 100644
--- a/internal/ceres/iterative_schur_complement_solver.cc
+++ b/internal/ceres/iterative_schur_complement_solver.cc
@@ -110,6 +110,8 @@
Preconditioner::Options preconditioner_options;
preconditioner_options.type = options_.preconditioner_type;
+ preconditioner_options.visibility_clustering_type =
+ options_.visibility_clustering_type;
preconditioner_options.sparse_linear_algebra_library_type =
options_.sparse_linear_algebra_library_type;
preconditioner_options.num_threads = options_.num_threads;
diff --git a/internal/ceres/linear_solver.h b/internal/ceres/linear_solver.h
index 22691b3..b0ab80d 100644
--- a/internal/ceres/linear_solver.h
+++ b/internal/ceres/linear_solver.h
@@ -74,6 +74,7 @@
Options()
: type(SPARSE_NORMAL_CHOLESKY),
preconditioner_type(JACOBI),
+ visibility_clustering_type(CANONICAL_VIEWS),
dense_linear_algebra_library_type(EIGEN),
sparse_linear_algebra_library_type(SUITE_SPARSE),
use_postordering(false),
@@ -89,6 +90,7 @@
LinearSolverType type;
PreconditionerType preconditioner_type;
+ VisibilityClusteringType visibility_clustering_type;
DenseLinearAlgebraLibraryType dense_linear_algebra_library_type;
SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;
diff --git a/internal/ceres/preconditioner.h b/internal/ceres/preconditioner.h
index af64e3c..21cbc00 100644
--- a/internal/ceres/preconditioner.h
+++ b/internal/ceres/preconditioner.h
@@ -48,6 +48,7 @@
struct Options {
Options()
: type(JACOBI),
+ visibility_clustering_type(CANONICAL_VIEWS),
sparse_linear_algebra_library_type(SUITE_SPARSE),
num_threads(1),
row_block_size(Eigen::Dynamic),
@@ -56,7 +57,7 @@
}
PreconditionerType type;
-
+ VisibilityClusteringType visibility_clustering_type;
SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;
// If possible, how many threads the preconditioner can use.
diff --git a/internal/ceres/single_linkage_clustering.cc b/internal/ceres/single_linkage_clustering.cc
new file mode 100644
index 0000000..54b4379
--- /dev/null
+++ b/internal/ceres/single_linkage_clustering.cc
@@ -0,0 +1,107 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+// * 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 Google Inc. 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"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#ifndef CERES_NO_SUITESPARSE
+
+#include "ceres/single_linkage_clustering.h"
+
+#include "ceres/graph.h"
+#include "ceres/collections_port.h"
+#include "ceres/graph_algorithms.h"
+
+namespace ceres {
+namespace internal {
+
+int ComputeSingleLinkageClustering(
+ const SingleLinkageClusteringOptions& options,
+ const Graph<int>& graph,
+ HashMap<int, int>* membership) {
+ CHECK_NOTNULL(membership)->clear();
+
+ // Initially each vertex is in its own cluster.
+ const HashSet<int>& vertices = graph.vertices();
+ for (HashSet<int>::const_iterator it = vertices.begin();
+ it != vertices.end();
+ ++it) {
+ (*membership)[*it] = *it;
+ }
+
+ for (HashSet<int>::const_iterator it1 = vertices.begin();
+ it1 != vertices.end();
+ ++it1) {
+ const int vertex1 = *it1;
+ const HashSet<int>& neighbors = graph.Neighbors(vertex1);
+ for (HashSet<int>::const_iterator it2 = neighbors.begin();
+ it2 != neighbors.end();
+ ++it2) {
+ const int vertex2 = *it2;
+
+ // Since the graph is undirected, only pay attention to one side
+ // of the edge and ignore weak edges.
+ if ((vertex1 > vertex2) ||
+ (graph.EdgeWeight(vertex1, vertex2) < options.min_similarity)) {
+ continue;
+ }
+
+ // Use a union-find algorithm to keep track of the clusters.
+ const int c1 = FindConnectedComponent(vertex1, membership);
+ const int c2 = FindConnectedComponent(vertex2, membership);
+
+ if (c1 == c2) {
+ continue;
+ }
+
+ if (c1 < c2) {
+ (*membership)[c2] = c1;
+ } else {
+ (*membership)[c1] = c2;
+ }
+ }
+ }
+
+ // Make sure that every vertex is connected directly to the vertex
+ // identifying the cluster.
+ int num_clusters = 0;
+ for (HashMap<int, int>::iterator it = membership->begin();
+ it != membership->end();
+ ++it) {
+ it->second = FindConnectedComponent(it->first, membership);
+ if (it->first == it->second) {
+ ++num_clusters;
+ }
+ }
+
+ return num_clusters;
+}
+
+} // namespace internal
+} // namespace ceres
+
+#endif // CERES_NO_SUITESPARSE
diff --git a/internal/ceres/single_linkage_clustering.h b/internal/ceres/single_linkage_clustering.h
new file mode 100644
index 0000000..a0fe8f9
--- /dev/null
+++ b/internal/ceres/single_linkage_clustering.h
@@ -0,0 +1,71 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+// * 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 Google Inc. 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"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#ifndef CERES_INTERNAL_SINGLE_LINKAGE_CLUSTERING_H_
+#define CERES_INTERNAL_SINGLE_LINKAGE_CLUSTERING_H_
+
+#ifndef CERES_NO_SUITESPARSE
+
+#include "ceres/collections_port.h"
+#include "ceres/graph.h"
+
+namespace ceres {
+namespace internal {
+
+struct SingleLinkageClusteringOptions {
+ SingleLinkageClusteringOptions()
+ : min_similarity(0.99) {
+ }
+
+ // Graph edges with edge weight less that min_similarity are ignored
+ // during the clustering process.
+ double min_similarity;
+};
+
+// Compute a partitioning of the vertices of the graph using the
+// single linkage clustering algorithm. Edges with weight less than
+// SingleLinkageClusteringOptions::min_similarity will be ignored.
+//
+// membership upon return will contain a mapping from the vertices of
+// the graph to an integer indicating the identity of the cluster that
+// it belongs to.
+//
+// The return value of this function is the number of clusters
+// identified by the algorithm.
+int ComputeSingleLinkageClustering(
+ const SingleLinkageClusteringOptions& options,
+ const Graph<int>& graph,
+ HashMap<int, int>* membership);
+
+} // namespace internal
+} // namespace ceres
+
+#endif // CERES_NO_SUITESPARSE
+#endif // CERES_INTERNAL_SINGLE_LINKAGE_CLUSTERING_H_
diff --git a/internal/ceres/single_linkage_clustering_test.cc b/internal/ceres/single_linkage_clustering_test.cc
new file mode 100644
index 0000000..8fbf84b
--- /dev/null
+++ b/internal/ceres/single_linkage_clustering_test.cc
@@ -0,0 +1,125 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+// * 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 Google Inc. 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"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: Sameer Agarwal (sameeragarwal@google.com)
+
+#ifndef CERES_NO_SUITESPARSE
+
+#include "ceres/single_linkage_clustering.h"
+
+#include "ceres/collections_port.h"
+#include "ceres/graph.h"
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+
+TEST(SingleLinkageClustering, GraphHasTwoComponents) {
+ Graph<int> graph;
+ const int kNumVertices = 6;
+ for (int i = 0; i < kNumVertices; ++i) {
+ graph.AddVertex(i);
+ }
+ // Graph structure:
+ //
+ // 0-1-2-3 4-5
+ graph.AddEdge(0, 1, 1.0);
+ graph.AddEdge(1, 2, 1.0);
+ graph.AddEdge(2, 3, 1.0);
+ graph.AddEdge(4, 5, 1.0);
+
+ SingleLinkageClusteringOptions options;
+ HashMap<int, int> membership;
+ ComputeSingleLinkageClustering(options, graph, &membership);
+ EXPECT_EQ(membership.size(), kNumVertices);
+
+ EXPECT_EQ(membership[1], membership[0]);
+ EXPECT_EQ(membership[2], membership[0]);
+ EXPECT_EQ(membership[3], membership[0]);
+ EXPECT_EQ(membership[4], membership[5]);
+}
+
+TEST(SingleLinkageClustering, ComponentWithWeakLink) {
+ Graph<int> graph;
+ const int kNumVertices = 6;
+ for (int i = 0; i < kNumVertices; ++i) {
+ graph.AddVertex(i);
+ }
+ // Graph structure:
+ //
+ // 0-1-2-3 4-5
+ graph.AddEdge(0, 1, 1.0);
+ graph.AddEdge(1, 2, 1.0);
+ graph.AddEdge(2, 3, 1.0);
+
+ // This component should break up into two.
+ graph.AddEdge(4, 5, 0.5);
+
+ SingleLinkageClusteringOptions options;
+ HashMap<int, int> membership;
+ ComputeSingleLinkageClustering(options, graph, &membership);
+ EXPECT_EQ(membership.size(), kNumVertices);
+
+ EXPECT_EQ(membership[1], membership[0]);
+ EXPECT_EQ(membership[2], membership[0]);
+ EXPECT_EQ(membership[3], membership[0]);
+ EXPECT_NE(membership[4], membership[5]);
+}
+
+TEST(SingleLinkageClustering, ComponentWithWeakLinkAndStrongLink) {
+ Graph<int> graph;
+ const int kNumVertices = 6;
+ for (int i = 0; i < kNumVertices; ++i) {
+ graph.AddVertex(i);
+ }
+ // Graph structure:
+ //
+ // 0-1-2-3 4-5
+ graph.AddEdge(0, 1, 1.0);
+ graph.AddEdge(1, 2, 1.0);
+ graph.AddEdge(2, 3, 0.5); // Weak link
+ graph.AddEdge(0, 3, 1.0);
+
+ // This component should break up into two.
+ graph.AddEdge(4, 5, 1.0);
+
+ SingleLinkageClusteringOptions options;
+ HashMap<int, int> membership;
+ ComputeSingleLinkageClustering(options, graph, &membership);
+ EXPECT_EQ(membership.size(), kNumVertices);
+
+ EXPECT_EQ(membership[1], membership[0]);
+ EXPECT_EQ(membership[2], membership[0]);
+ EXPECT_EQ(membership[3], membership[0]);
+ EXPECT_EQ(membership[4], membership[5]);
+}
+
+} // namespace internal
+} // namespace ceres
+
+#endif // CERES_NO_SUITESPARSE
diff --git a/internal/ceres/solver.cc b/internal/ceres/solver.cc
index 3b67746..2750e23 100644
--- a/internal/ceres/solver.cc
+++ b/internal/ceres/solver.cc
@@ -119,6 +119,7 @@
inner_iterations_given(false),
inner_iterations_used(false),
preconditioner_type(IDENTITY),
+ visibility_clustering_type(CANONICAL_VIEWS),
trust_region_strategy_type(LEVENBERG_MARQUARDT),
dense_linear_algebra_library_type(EIGEN),
sparse_linear_algebra_library_type(SUITE_SPARSE),
@@ -237,6 +238,12 @@
PreconditionerTypeToString(preconditioner_type));
}
+ if (preconditioner_type == CLUSTER_JACOBI ||
+ preconditioner_type == CLUSTER_TRIDIAGONAL) {
+ StringAppendF(&report, "Visibility clustering%24s%25s\n",
+ VisibilityClusteringTypeToString(visibility_clustering_type),
+ VisibilityClusteringTypeToString(visibility_clustering_type));
+ }
StringAppendF(&report, "Threads % 25d% 25d\n",
num_threads_given, num_threads_used);
StringAppendF(&report, "Linear solver threads % 23d% 25d\n",
diff --git a/internal/ceres/solver_impl.cc b/internal/ceres/solver_impl.cc
index ae7fc3f..f544f8f 100644
--- a/internal/ceres/solver_impl.cc
+++ b/internal/ceres/solver_impl.cc
@@ -512,6 +512,7 @@
summary->linear_solver_type_used = options.linear_solver_type;
summary->preconditioner_type = options.preconditioner_type;
+ summary->visibility_clustering_type = options.visibility_clustering_type;
summary->num_linear_solver_threads_given =
original_options.num_linear_solver_threads;
@@ -1244,6 +1245,8 @@
options->max_linear_solver_iterations;
linear_solver_options.type = options->linear_solver_type;
linear_solver_options.preconditioner_type = options->preconditioner_type;
+ linear_solver_options.visibility_clustering_type =
+ options->visibility_clustering_type;
linear_solver_options.sparse_linear_algebra_library_type =
options->sparse_linear_algebra_library_type;
linear_solver_options.dense_linear_algebra_library_type =
diff --git a/internal/ceres/types.cc b/internal/ceres/types.cc
index a97f1a5..80f8d39 100644
--- a/internal/ceres/types.cc
+++ b/internal/ceres/types.cc
@@ -278,6 +278,25 @@
return false;
}
+const char* VisibilityClusteringTypeToString(
+ VisibilityClusteringType type) {
+ switch (type) {
+ CASESTR(CANONICAL_VIEWS);
+ CASESTR(SINGLE_LINKAGE);
+ default:
+ return "UNKNOWN";
+ }
+}
+
+bool StringToVisibilityClusteringType(
+ string value,
+ VisibilityClusteringType* type) {
+ UpperCase(&value);
+ STRENUM(CANONICAL_VIEWS);
+ STRENUM(SINGLE_LINKAGE);
+ return false;
+}
+
const char* SolverTerminationTypeToString(SolverTerminationType type) {
switch (type) {
CASESTR(NO_CONVERGENCE);
diff --git a/internal/ceres/visibility_based_preconditioner.cc b/internal/ceres/visibility_based_preconditioner.cc
index 6535b42..c760c5b 100644
--- a/internal/ceres/visibility_based_preconditioner.cc
+++ b/internal/ceres/visibility_based_preconditioner.cc
@@ -48,6 +48,7 @@
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_solver.h"
#include "ceres/schur_eliminator.h"
+#include "ceres/single_linkage_clustering.h"
#include "ceres/visibility.h"
#include "glog/logging.h"
@@ -60,8 +61,9 @@
//
// This will require some more work on the clustering algorithm and
// possibly some more refactoring of the code.
-static const double kSizePenaltyWeight = 3.0;
-static const double kSimilarityPenaltyWeight = 0.0;
+static const double kCanonicalViewsSizePenaltyWeight = 3.0;
+static const double kCanonicalViewsSimilarityPenaltyWeight = 0.0;
+static const double kSingleLinkageMinSimilarity = 0.9;
VisibilityBasedPreconditioner::VisibilityBasedPreconditioner(
const CompressedRowBlockStructure& bs,
@@ -187,17 +189,29 @@
scoped_ptr<Graph<int> > schur_complement_graph(
CHECK_NOTNULL(CreateSchurComplementGraph(visibility)));
- CanonicalViewsClusteringOptions options;
- options.size_penalty_weight = kSizePenaltyWeight;
- options.similarity_penalty_weight = kSimilarityPenaltyWeight;
-
- vector<int> centers;
HashMap<int, int> membership;
- ComputeCanonicalViewsClustering(*schur_complement_graph,
- options,
- ¢ers,
- &membership);
- num_clusters_ = centers.size();
+
+ if (options_.visibility_clustering_type == CANONICAL_VIEWS) {
+ vector<int> centers;
+ CanonicalViewsClusteringOptions clustering_options;
+ clustering_options.size_penalty_weight =
+ kCanonicalViewsSizePenaltyWeight;
+ clustering_options.similarity_penalty_weight =
+ kCanonicalViewsSimilarityPenaltyWeight;
+ ComputeCanonicalViewsClustering(clustering_options,
+ *schur_complement_graph,
+ ¢ers,
+ &membership);
+ num_clusters_ = centers.size();
+ } else if (options_.visibility_clustering_type == SINGLE_LINKAGE) {
+ SingleLinkageClusteringOptions clustering_options;
+ clustering_options.min_similarity =
+ kSingleLinkageMinSimilarity;
+ num_clusters_ = ComputeSingleLinkageClustering(clustering_options,
+ *schur_complement_graph,
+ &membership);
+ }
+
CHECK_GT(num_clusters_, 0);
VLOG(2) << "num_clusters: " << num_clusters_;
FlattenMembershipMap(membership, &cluster_membership_);
@@ -542,11 +556,17 @@
// cluster ids. Convert this into a flat array for quick lookup. It is
// possible that some of the vertices may not be associated with any
// cluster. In that case, randomly assign them to one of the clusters.
+//
+// The cluster ids can be non-contiguous integers. So as we flatten
+// the membership_map, we also map the cluster ids to a contiguous set
+// of integers so that the cluster ids are in [0, num_clusters_).
void VisibilityBasedPreconditioner::FlattenMembershipMap(
const HashMap<int, int>& membership_map,
vector<int>* membership_vector) const {
CHECK_NOTNULL(membership_vector)->resize(0);
membership_vector->resize(num_blocks_, -1);
+
+ HashMap<int, int> cluster_id_to_index;
// Iterate over the cluster membership map and update the
// cluster_membership_ vector assigning arbitrary cluster ids to
// the few cameras that have not been clustered.
@@ -567,7 +587,17 @@
cluster_id = camera_id % num_clusters_;
}
- membership_vector->at(camera_id) = cluster_id;
+ const int index = FindWithDefault(cluster_id_to_index,
+ cluster_id,
+ cluster_id_to_index.size());
+
+ if (index == cluster_id_to_index.size()) {
+ cluster_id_to_index[cluster_id] = index;
+ CHECK_NE(index, cluster_id_to_index.size());
+ }
+
+ CHECK_NE(index, num_clusters_);
+ membership_vector->at(camera_id) = index;
}
}
