// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
// This include must come before any #ifndef check on Ceres compile options.
#include "ceres/internal/port.h"
#ifndef CERES_NO_SUITESPARSE
#include "ceres/visibility_based_preconditioner.h"
#include <algorithm>
#include <functional>
#include <iterator>
#include <set>
#include <utility>
#include <vector>
#include "Eigen/Dense"
#include "ceres/block_random_access_sparse_matrix.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/canonical_views_clustering.h"
#include "ceres/collections_port.h"
#include "ceres/graph.h"
#include "ceres/graph_algorithms.h"
#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"
namespace ceres {
namespace internal {
// TODO(sameeragarwal): Currently these are magic weights for the
// preconditioner construction. Move these higher up into the Options
// struct and provide some guidelines for choosing them.
//
// This will require some more work on the clustering algorithm and
// possibly some more refactoring of the code.
static const double kCanonicalViewsSizePenaltyWeight = 3.0;
static const double kCanonicalViewsSimilarityPenaltyWeight = 0.0;
static const double kSingleLinkageMinSimilarity = 0.9;
VisibilityBasedPreconditioner::VisibilityBasedPreconditioner(
const CompressedRowBlockStructure& bs,
const Preconditioner::Options& options)
: options_(options),
num_blocks_(0),
num_clusters_(0),
factor_(NULL) {
CHECK_GT(options_.elimination_groups.size(), 1);
CHECK_GT(options_.elimination_groups[0], 0);
CHECK(options_.type == CLUSTER_JACOBI ||
options_.type == CLUSTER_TRIDIAGONAL)
<< "Unknown preconditioner type: " << options_.type;
num_blocks_ = bs.cols.size() - options_.elimination_groups[0];
CHECK_GT(num_blocks_, 0)
<< "Jacobian should have atleast 1 f_block for "
<< "visibility based preconditioning.";
// Vector of camera block sizes
block_size_.resize(num_blocks_);
for (int i = 0; i < num_blocks_; ++i) {
block_size_[i] = bs.cols[i + options_.elimination_groups[0]].size;
}
const time_t start_time = time(NULL);
switch (options_.type) {
case CLUSTER_JACOBI:
ComputeClusterJacobiSparsity(bs);
break;
case CLUSTER_TRIDIAGONAL:
ComputeClusterTridiagonalSparsity(bs);
break;
default:
LOG(FATAL) << "Unknown preconditioner type";
}
const time_t structure_time = time(NULL);
InitStorage(bs);
const time_t storage_time = time(NULL);
InitEliminator(bs);
const time_t eliminator_time = time(NULL);
// Allocate temporary storage for a vector used during
// RightMultiply.
tmp_rhs_ = CHECK_NOTNULL(ss_.CreateDenseVector(NULL,
m_->num_rows(),
m_->num_rows()));
const time_t init_time = time(NULL);
VLOG(2) << "init time: "
<< init_time - start_time
<< " structure time: " << structure_time - start_time
<< " storage time:" << storage_time - structure_time
<< " eliminator time: " << eliminator_time - storage_time;
}
VisibilityBasedPreconditioner::~VisibilityBasedPreconditioner() {
if (factor_ != NULL) {
ss_.Free(factor_);
factor_ = NULL;
}
if (tmp_rhs_ != NULL) {
ss_.Free(tmp_rhs_);
tmp_rhs_ = NULL;
}
}
// Determine the sparsity structure of the CLUSTER_JACOBI
// preconditioner. It clusters cameras using their scene
// visibility. The clusters form the diagonal blocks of the
// preconditioner matrix.
void VisibilityBasedPreconditioner::ComputeClusterJacobiSparsity(
const CompressedRowBlockStructure& bs) {
vector<set<int> > visibility;
ComputeVisibility(bs, options_.elimination_groups[0], &visibility);
CHECK_EQ(num_blocks_, visibility.size());
ClusterCameras(visibility);
cluster_pairs_.clear();
for (int i = 0; i < num_clusters_; ++i) {
cluster_pairs_.insert(make_pair(i, i));
}
}
// Determine the sparsity structure of the CLUSTER_TRIDIAGONAL
// preconditioner. It clusters cameras using using the scene
// visibility and then finds the strongly interacting pairs of
// clusters by constructing another graph with the clusters as
// vertices and approximating it with a degree-2 maximum spanning
// forest. The set of edges in this forest are the cluster pairs.
void VisibilityBasedPreconditioner::ComputeClusterTridiagonalSparsity(
const CompressedRowBlockStructure& bs) {
vector<set<int> > visibility;
ComputeVisibility(bs, options_.elimination_groups[0], &visibility);
CHECK_EQ(num_blocks_, visibility.size());
ClusterCameras(visibility);
// Construct a weighted graph on the set of clusters, where the
// edges are the number of 3D points/e_blocks visible in both the
// clusters at the ends of the edge. Return an approximate degree-2
// maximum spanning forest of this graph.
vector<set<int> > cluster_visibility;
ComputeClusterVisibility(visibility, &cluster_visibility);
scoped_ptr<Graph<int> > cluster_graph(
CHECK_NOTNULL(CreateClusterGraph(cluster_visibility)));
scoped_ptr<Graph<int> > forest(
CHECK_NOTNULL(Degree2MaximumSpanningForest(*cluster_graph)));
ForestToClusterPairs(*forest, &cluster_pairs_);
}
// Allocate storage for the preconditioner matrix.
void VisibilityBasedPreconditioner::InitStorage(
const CompressedRowBlockStructure& bs) {
ComputeBlockPairsInPreconditioner(bs);
m_.reset(new BlockRandomAccessSparseMatrix(block_size_, block_pairs_));
}
// Call the canonical views algorithm and cluster the cameras based on
// their visibility sets. The visibility set of a camera is the set of
// e_blocks/3D points in the scene that are seen by it.
//
// The cluster_membership_ vector is updated to indicate cluster
// memberships for each camera block.
void VisibilityBasedPreconditioner::ClusterCameras(
const vector<set<int> >& visibility) {
scoped_ptr<Graph<int> > schur_complement_graph(
CHECK_NOTNULL(CreateSchurComplementGraph(visibility)));
HashMap<int, int> membership;
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);
} else {
LOG(FATAL) << "Unknown visibility clustering algorithm.";
}
CHECK_GT(num_clusters_, 0);
VLOG(2) << "num_clusters: " << num_clusters_;
FlattenMembershipMap(membership, &cluster_membership_);
}
// Compute the block sparsity structure of the Schur complement
// matrix. For each pair of cameras contributing a non-zero cell to
// the schur complement, determine if that cell is present in the
// preconditioner or not.
//
// A pair of cameras contribute a cell to the preconditioner if they
// are part of the same cluster or if the the two clusters that they
// belong have an edge connecting them in the degree-2 maximum
// spanning forest.
//
// For example, a camera pair (i,j) where i belonges to cluster1 and
// j belongs to cluster2 (assume that cluster1 < cluster2).
//
// The cell corresponding to (i,j) is present in the preconditioner
// if cluster1 == cluster2 or the pair (cluster1, cluster2) were
// connected by an edge in the degree-2 maximum spanning forest.
//
// Since we have already expanded the forest into a set of camera
// pairs/edges, including self edges, the check can be reduced to
// checking membership of (cluster1, cluster2) in cluster_pairs_.
void VisibilityBasedPreconditioner::ComputeBlockPairsInPreconditioner(
const CompressedRowBlockStructure& bs) {
block_pairs_.clear();
for (int i = 0; i < num_blocks_; ++i) {
block_pairs_.insert(make_pair(i, i));
}
int r = 0;
const int num_row_blocks = bs.rows.size();
const int num_eliminate_blocks = options_.elimination_groups[0];
// Iterate over each row of the matrix. The block structure of the
// matrix is assumed to be sorted in order of the e_blocks/point
// blocks. Thus all row blocks containing an e_block/point occur
// contiguously. Further, if present, an e_block is always the first
// parameter block in each row block. These structural assumptions
// are common to all Schur complement based solvers in Ceres.
//
// For each e_block/point block we identify the set of cameras
// seeing it. The cross product of this set with itself is the set
// of non-zero cells contibuted by this e_block.
//
// The time complexity of this is O(nm^2) where, n is the number of
// 3d points and m is the maximum number of cameras seeing any
// point, which for most scenes is a fairly small number.
while (r < num_row_blocks) {
int e_block_id = bs.rows[r].cells.front().block_id;
if (e_block_id >= num_eliminate_blocks) {
// Skip the rows whose first block is an f_block.
break;
}
set<int> f_blocks;
for (; r < num_row_blocks; ++r) {
const CompressedRow& row = bs.rows[r];
if (row.cells.front().block_id != e_block_id) {
break;
}
// Iterate over the blocks in the row, ignoring the first block
// since it is the one to be eliminated and adding the rest to
// the list of f_blocks associated with this e_block.
for (int c = 1; c < row.cells.size(); ++c) {
const Cell& cell = row.cells[c];
const int f_block_id = cell.block_id - num_eliminate_blocks;
CHECK_GE(f_block_id, 0);
f_blocks.insert(f_block_id);
}
}
for (set<int>::const_iterator block1 = f_blocks.begin();
block1 != f_blocks.end();
++block1) {
set<int>::const_iterator block2 = block1;
++block2;
for (; block2 != f_blocks.end(); ++block2) {
if (IsBlockPairInPreconditioner(*block1, *block2)) {
block_pairs_.insert(make_pair(*block1, *block2));
}
}
}
}
// The remaining rows which do not contain any e_blocks.
for (; r < num_row_blocks; ++r) {
const CompressedRow& row = bs.rows[r];
CHECK_GE(row.cells.front().block_id, num_eliminate_blocks);
for (int i = 0; i < row.cells.size(); ++i) {
const int block1 = row.cells[i].block_id - num_eliminate_blocks;
for (int j = 0; j < row.cells.size(); ++j) {
const int block2 = row.cells[j].block_id - num_eliminate_blocks;
if (block1 <= block2) {
if (IsBlockPairInPreconditioner(block1, block2)) {
block_pairs_.insert(make_pair(block1, block2));
}
}
}
}
}
VLOG(1) << "Block pair stats: " << block_pairs_.size();
}
// Initialize the SchurEliminator.
void VisibilityBasedPreconditioner::InitEliminator(
const CompressedRowBlockStructure& bs) {
LinearSolver::Options eliminator_options;
eliminator_options.elimination_groups = options_.elimination_groups;
eliminator_options.num_threads = options_.num_threads;
eliminator_options.e_block_size = options_.e_block_size;
eliminator_options.f_block_size = options_.f_block_size;
eliminator_options.row_block_size = options_.row_block_size;
eliminator_.reset(SchurEliminatorBase::Create(eliminator_options));
eliminator_->Init(eliminator_options.elimination_groups[0], &bs);
}
// Update the values of the preconditioner matrix and factorize it.
bool VisibilityBasedPreconditioner::UpdateImpl(const BlockSparseMatrix& A,
const double* D) {
const time_t start_time = time(NULL);
const int num_rows = m_->num_rows();
CHECK_GT(num_rows, 0);
// We need a dummy rhs vector and a dummy b vector since the Schur
// eliminator combines the computation of the reduced camera matrix
// with the computation of the right hand side of that linear
// system.
//
// TODO(sameeragarwal): Perhaps its worth refactoring the
// SchurEliminator::Eliminate function to allow NULL for the rhs. As
// of now it does not seem to be worth the effort.
Vector rhs = Vector::Zero(m_->num_rows());
Vector b = Vector::Zero(A.num_rows());
// Compute a subset of the entries of the Schur complement.
eliminator_->Eliminate(&A, b.data(), D, m_.get(), rhs.data());
// Try factorizing the matrix. For CLUSTER_JACOBI, this should
// always succeed modulo some numerical/conditioning problems. For
// CLUSTER_TRIDIAGONAL, in general the preconditioner matrix as
// constructed is not positive definite. However, we will go ahead
// and try factorizing it. If it works, great, otherwise we scale
// all the cells in the preconditioner corresponding to the edges in
// the degree-2 forest and that guarantees positive
// definiteness. The proof of this fact can be found in Lemma 1 in
// "Visibility Based Preconditioning for Bundle Adjustment".
//
// Doing the factorization like this saves us matrix mass when
// scaling is not needed, which is quite often in our experience.
LinearSolverTerminationType status = Factorize();
if (status == LINEAR_SOLVER_FATAL_ERROR) {
return false;
}
// The scaling only affects the tri-diagonal case, since
// ScaleOffDiagonalBlocks only pays attenion to the cells that
// belong to the edges of the degree-2 forest. In the CLUSTER_JACOBI
// case, the preconditioner is guaranteed to be positive
// semidefinite.
if (status == LINEAR_SOLVER_FAILURE && options_.type == CLUSTER_TRIDIAGONAL) {
VLOG(1) << "Unscaled factorization failed. Retrying with off-diagonal "
<< "scaling";
ScaleOffDiagonalCells();
status = Factorize();
}
VLOG(2) << "Compute time: " << time(NULL) - start_time;
return (status == LINEAR_SOLVER_SUCCESS);
}
// Consider the preconditioner matrix as meta-block matrix, whose
// blocks correspond to the clusters. Then cluster pairs corresponding
// to edges in the degree-2 forest are off diagonal entries of this
// matrix. Scaling these off-diagonal entries by 1/2 forces this
// matrix to be positive definite.
void VisibilityBasedPreconditioner::ScaleOffDiagonalCells() {
for (set< pair<int, int> >::const_iterator it = block_pairs_.begin();
it != block_pairs_.end();
++it) {
const int block1 = it->first;
const int block2 = it->second;
if (!IsBlockPairOffDiagonal(block1, block2)) {
continue;
}
int r, c, row_stride, col_stride;
CellInfo* cell_info = m_->GetCell(block1, block2,
&r, &c,
&row_stride, &col_stride);
CHECK(cell_info != NULL)
<< "Cell missing for block pair (" << block1 << "," << block2 << ")"
<< " cluster pair (" << cluster_membership_[block1]
<< " " << cluster_membership_[block2] << ")";
// Ah the magic of tri-diagonal matrices and diagonal
// dominance. See Lemma 1 in "Visibility Based Preconditioning
// For Bundle Adjustment".
MatrixRef m(cell_info->values, row_stride, col_stride);
m.block(r, c, block_size_[block1], block_size_[block2]) *= 0.5;
}
}
// Compute the sparse Cholesky factorization of the preconditioner
// matrix.
LinearSolverTerminationType VisibilityBasedPreconditioner::Factorize() {
// Extract the TripletSparseMatrix that is used for actually storing
// S and convert it into a cholmod_sparse object.
cholmod_sparse* lhs = ss_.CreateSparseMatrix(
down_cast<BlockRandomAccessSparseMatrix*>(
m_.get())->mutable_matrix());
// The matrix is symmetric, and the upper triangular part of the
// matrix contains the values.
lhs->stype = 1;
// TODO(sameeragarwal): Refactor to pipe this up and out.
string status;
// Symbolic factorization is computed if we don't already have one handy.
if (factor_ == NULL) {
factor_ = ss_.BlockAnalyzeCholesky(lhs, block_size_, block_size_, &status);
}
const LinearSolverTerminationType termination_type =
(factor_ != NULL)
? ss_.Cholesky(lhs, factor_, &status)
: LINEAR_SOLVER_FATAL_ERROR;
ss_.Free(lhs);
return termination_type;
}
void VisibilityBasedPreconditioner::RightMultiply(const double* x,
double* y) const {
CHECK_NOTNULL(x);
CHECK_NOTNULL(y);
SuiteSparse* ss = const_cast<SuiteSparse*>(&ss_);
const int num_rows = m_->num_rows();
memcpy(CHECK_NOTNULL(tmp_rhs_)->x, x, m_->num_rows() * sizeof(*x));
// TODO(sameeragarwal): Better error handling.
string status;
cholmod_dense* solution =
CHECK_NOTNULL(ss->Solve(factor_, tmp_rhs_, &status));
memcpy(y, solution->x, sizeof(*y) * num_rows);
ss->Free(solution);
}
int VisibilityBasedPreconditioner::num_rows() const {
return m_->num_rows();
}
// Classify camera/f_block pairs as in and out of the preconditioner,
// based on whether the cluster pair that they belong to is in the
// preconditioner or not.
bool VisibilityBasedPreconditioner::IsBlockPairInPreconditioner(
const int block1,
const int block2) const {
int cluster1 = cluster_membership_[block1];
int cluster2 = cluster_membership_[block2];
if (cluster1 > cluster2) {
std::swap(cluster1, cluster2);
}
return (cluster_pairs_.count(make_pair(cluster1, cluster2)) > 0);
}
bool VisibilityBasedPreconditioner::IsBlockPairOffDiagonal(
const int block1,
const int block2) const {
return (cluster_membership_[block1] != cluster_membership_[block2]);
}
// Convert a graph into a list of edges that includes self edges for
// each vertex.
void VisibilityBasedPreconditioner::ForestToClusterPairs(
const Graph<int>& forest,
HashSet<pair<int, int> >* cluster_pairs) const {
CHECK_NOTNULL(cluster_pairs)->clear();
const HashSet<int>& vertices = forest.vertices();
CHECK_EQ(vertices.size(), num_clusters_);
// Add all the cluster pairs corresponding to the edges in the
// forest.
for (HashSet<int>::const_iterator it1 = vertices.begin();
it1 != vertices.end();
++it1) {
const int cluster1 = *it1;
cluster_pairs->insert(make_pair(cluster1, cluster1));
const HashSet<int>& neighbors = forest.Neighbors(cluster1);
for (HashSet<int>::const_iterator it2 = neighbors.begin();
it2 != neighbors.end();
++it2) {
const int cluster2 = *it2;
if (cluster1 < cluster2) {
cluster_pairs->insert(make_pair(cluster1, cluster2));
}
}
}
}
// The visibilty set of a cluster is the union of the visibilty sets
// of all its cameras. In other words, the set of points visible to
// any camera in the cluster.
void VisibilityBasedPreconditioner::ComputeClusterVisibility(
const vector<set<int> >& visibility,
vector<set<int> >* cluster_visibility) const {
CHECK_NOTNULL(cluster_visibility)->resize(0);
cluster_visibility->resize(num_clusters_);
for (int i = 0; i < num_blocks_; ++i) {
const int cluster_id = cluster_membership_[i];
(*cluster_visibility)[cluster_id].insert(visibility[i].begin(),
visibility[i].end());
}
}
// Construct a graph whose vertices are the clusters, and the edge
// weights are the number of 3D points visible to cameras in both the
// vertices.
Graph<int>* VisibilityBasedPreconditioner::CreateClusterGraph(
const vector<set<int> >& cluster_visibility) const {
Graph<int>* cluster_graph = new Graph<int>;
for (int i = 0; i < num_clusters_; ++i) {
cluster_graph->AddVertex(i);
}
for (int i = 0; i < num_clusters_; ++i) {
const set<int>& cluster_i = cluster_visibility[i];
for (int j = i+1; j < num_clusters_; ++j) {
vector<int> intersection;
const set<int>& cluster_j = cluster_visibility[j];
set_intersection(cluster_i.begin(), cluster_i.end(),
cluster_j.begin(), cluster_j.end(),
back_inserter(intersection));
if (intersection.size() > 0) {
// Clusters interact strongly when they share a large number
// of 3D points. The degree-2 maximum spanning forest
// alorithm, iterates on the edges in decreasing order of
// their weight, which is the number of points shared by the
// two cameras that it connects.
cluster_graph->AddEdge(i, j, intersection.size());
}
}
}
return cluster_graph;
}
// Canonical views clustering returns a HashMap from vertices to
// 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.
for (HashMap<int, int>::const_iterator it = membership_map.begin();
it != membership_map.end();
++it) {
const int camera_id = it->first;
int cluster_id = it->second;
// If the view was not clustered, randomly assign it to one of the
// clusters. This preserves the mathematical correctness of the
// preconditioner. If there are too many views which are not
// clustered, it may lead to some quality degradation though.
//
// TODO(sameeragarwal): Check if a large number of views have not
// been clustered and deal with it?
if (cluster_id == -1) {
cluster_id = camera_id % num_clusters_;
}
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_LT(index, num_clusters_);
membership_vector->at(camera_id) = index;
}
}
} // namespace internal
} // namespace ceres
#endif // CERES_NO_SUITESPARSE