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AvatarOptimizer.cpp
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AvatarOptimizer.cpp
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#include "AvatarOptimizer.h"
#include <vector>
#include <mutex>
#include <atomic>
#include <thread>
#include <iostream>
#include <Eigen/StdVector>
#include <ceres/ceres.h>
#include <nanoflann.hpp>
#include <boost/thread.hpp>
#include "Version.h"
#include "Avatar.h"
#include "AvatarRenderer.h"
#include "Util.h"
// #define PCL_DEBUG_VISUALIZE
#ifndef OPENARK_PCL_ENABLED
// If PCL not available then cannot visualize
#undef PCL_DEBUG_VISUALIZE
#endif
#ifdef PCL_DEBUG_VISUALIZE
#include <pcl/visualization/pcl_visualizer.h>
#endif
#define BEGIN_PROFILE //auto start = std::chrono::high_resolution_clock::now()
#define PROFILE(x) //do{printf("%s: %f ms\n", #x, std::chrono::duration<double, std::milli>(std::chrono::high_resolution_clock::now() - start).count()); start = std::chrono::high_resolution_clock::now(); }while(false)
// Uncomment below line to compare analytic diff results to auto diff
//#define TEST_COMPARE_AUTO_DIFF
namespace nanoflann {
/// KD-tree adaptor for working with data directly stored in a column-major Eigen Matrix, without duplicating the data storage.
/// This code is adapted from the KDTreeEigenMatrixAdaptor class of nanoflann.hpp
template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2_Simple, typename IndexType = int>
struct KDTreeEigenColMajorMatrixAdaptor {
typedef KDTreeEigenColMajorMatrixAdaptor<MatrixType, DIM, Distance> self_t;
typedef typename MatrixType::Scalar num_t;
typedef typename Distance::template traits<num_t,self_t>::distance_t metric_t;
typedef KDTreeSingleIndexAdaptor< metric_t,self_t,DIM,IndexType> index_t;
index_t* index;
KDTreeEigenColMajorMatrixAdaptor(const MatrixType &mat, const int leaf_max_size = 10) : m_data_matrix(mat) {
const size_t dims = mat.rows();
index = new index_t(dims, *this, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
index->buildIndex();
}
~KDTreeEigenColMajorMatrixAdaptor() {delete index;}
const MatrixType &m_data_matrix;
/// Query for the num_closest closest points to a given point (entered as query_point[0:dim-1]).
inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq) const {
nanoflann::KNNResultSet<typename MatrixType::Scalar,IndexType> resultSet(num_closest);
resultSet.init(out_indices, out_distances_sq);
index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
}
/// Query for the closest points to a given point (entered as query_point[0:dim-1]).
inline IndexType closest(const num_t *query_point) const {
IndexType out_indices;
num_t out_distances_sq;
query(query_point, 1, &out_indices, &out_distances_sq);
return out_indices;
}
const self_t & derived() const {return *this;}
self_t & derived() {return *this;}
inline size_t kdtree_get_point_count() const {return m_data_matrix.cols();}
/// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline num_t kdtree_distance(const num_t *p1, const size_t idx_p2,size_t size) const {
num_t s=0;
for (size_t i=0; i<size; i++) {
const num_t d= p1[i]-m_data_matrix.coeff(i,idx_p2);
s+=d*d; }
return s;
}
/// Returns the dim'th component of the idx'th point in the class:
inline num_t kdtree_get_pt(const size_t idx, int dim) const {
return m_data_matrix.coeff(dim,idx);
}
/// Optional bounding-box computation: return false to default to a standard bbox computation loop.
template <class BBOX> bool kdtree_get_bbox(BBOX&) const {return false;}
};
} // namespace nanoflann
namespace ceres {
/** WHY THIS? For our use case it is desirable to pre-compute the full Jacobian at an earlier stage but
* make use of the special addition operator for local parameterization. This is because some
* Jacobians are easier to compute wrt the local 'error' vector than the state. This is a known Ceres
* limitation, see https://groups.google.com/forum/#!msg/ceres-solver/fs8iNI9_F7Q/gv0R4NGLAwAJ
* Here, we set Jacobian to a dummy identity matrix, as suggested by a post on that page. The
* local Jacobian is multiplied manually in AvatarEvaluationCommonData where required. */
class FakeQuaternionParameterization : public ceres::LocalParameterization {
public:
~FakeQuaternionParameterization() {}
bool Plus(const double* x_ptr,
const double* delta,
double* x_plus_delta_ptr) const {
// Copied from EigenQuaternionParameterization
Eigen::Map<Eigen::Quaterniond> x_plus_delta(x_plus_delta_ptr);
Eigen::Map<const Eigen::Quaterniond> x(x_ptr);
const double norm_delta =
sqrt(delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2]);
if (norm_delta > 0.0) {
const double sin_delta_by_delta = sin(norm_delta) / norm_delta;
// Note, in the constructor w is first.
Eigen::Quaterniond delta_q(cos(norm_delta),
sin_delta_by_delta * delta[0],
sin_delta_by_delta * delta[1],
sin_delta_by_delta * delta[2]);
x_plus_delta = delta_q * x;
} else {
x_plus_delta = x;
}
return true;
}
bool ComputeJacobian(const double* x, double* jacobian) const {
Eigen::Map<Eigen::Matrix<double, 4, 3, Eigen::RowMajor> > J(jacobian);
J.topLeftCorner<3,3>().setIdentity();
J.bottomRows<1>().setZero();
return true;
}
int GlobalSize() const { return 4; }
int LocalSize() const { return 3; }
};
}
namespace ark {
namespace {
using MatAlloc = Eigen::aligned_allocator<Eigen::Matrix3d>;
using VecAlloc = Eigen::aligned_allocator<Eigen::Vector3d>;
/** Evaluation callback for Ceres */
template<class Cache>
struct AvatarEvaluationCommonData : public ceres::EvaluationCallback {
/** Max number of joint assignments to consider for each joint */
static const int MAX_ASSIGN = 4;
/** If true, will recalculate shape every update */
const bool shapeEnabled;
AvatarEvaluationCommonData(AvatarOptimizer& opt, bool shape_enabled = false)
: opt(opt), ava(opt.ava), numJointSpaces(opt.ava.model.numJoints() + 1), shapeEnabled(shape_enabled) {
// _L.resize(nJoints * AvatarOptimizer::ROT_SIZE);
localJacobian.resize(numJointSpaces);
if (shape_enabled) {
S.resize(ava.model.numJoints());
Sp.resize(ava.model.numJoints());
H.resize(ava.model.numJoints());
}
_R.resize(numJointSpaces * numJointSpaces);
_t.resize(_R.size());
shapedCloud.resize(3, ava.model.numPoints());
CalcShape();
// Make a list of deduplicated ancestor joints for each point
ancestor.resize(ava.model.numPoints());
for (int point = 0; point < ava.model.numPoints(); ++point) {
auto& ances = ancestor[point];
for (auto & weight_joint : ava.model.assignedJoints[point]) {
double weight = weight_joint.first;
int joint = weight_joint.second;
ances.emplace_back(joint, joint, weight);
for (int j = ava.model.parent[joint]; j != -1; j = ava.model.parent[j]) {
ances.emplace_back(j, joint, weight);
}
}
std::sort(ances.begin(), ances.end());
int last = 0;
for (size_t i = 1; i < ances.size(); ++i) {
if (ances[last].jid == ances[i].jid) {
ances[last].merge(ances[i]);
} else {
++last;
if (last < i) {
ances[last] = std::move(ances[i]);
}
}
}
ances.resize(last + 1);
}
if (shape_enabled) {
for (int j = 0; j < ava.model.numJoints(); ++j) {
Sp[j].resize(3, ava.model.numShapeKeys());
S[j].resize(3, ava.model.numShapeKeys());
H[j].resize(3, ava.model.numShapeKeys());
}
if (ava.model.useJointShapeRegressor) {
for (int j = 0; j < ava.model.numJoints(); ++j) {
S[j].noalias() = ava.model.jointShapeReg.middleRows<3>(3 * j);
}
} else {
for (int i = 0; i < ava.model.numShapeKeys(); ++i) {
Eigen::MatrixXd::Scalar d;
Eigen::Map<const CloudType> keyCloud(ava.model.keyClouds.data() + i * ava.model.numPoints() * 3,
3, ava.model.numPoints());
for (int j = 0; j < ava.model.numJoints(); ++j) {
S[j].col(i).noalias() = keyCloud * ava.model.jointRegressor.col(j);
}
}
}
for (int j = 1; j < ava.model.numJoints(); ++j) {
if (j) Sp[j].noalias() = S[j] - S[ava.model.parent[j]];
}
Sp[0].setZero();
H[0].setZero();
}
}
/** Compute joint and point positions after applying shape keys*/
void CalcShape() {
Eigen::Map<Eigen::VectorXd> shapedCloudVec(shapedCloud.data(), 3 * shapedCloud.cols());
/** Apply shape keys */
shapedCloudVec.noalias() = ava.model.keyClouds * ava.w + ava.model.baseCloud;
/** Apply joint [shape] regressor */
// TODO: use dense joint regressor with compressed cloud
if (ava.model.useJointShapeRegressor) {
jointPosInit.resize(3, ava.model.numJoints());
Eigen::Map<Eigen::VectorXd> jointPosVec(jointPosInit.data(), 3 * ava.model.numJoints());
jointPosVec.noalias() = ava.model.jointShapeRegBase + ava.model.jointShapeReg * ava.w;
} else {
jointPosInit.noalias() = shapedCloud * ava.model.jointRegressor;
}
/** Ensure root is at origin*/
Eigen::Vector3d offset = jointPosInit.col(0);
shapedCloud.colwise() -= offset;
jointPosInit.colwise() -= offset;
/** Find relative positions of each joint */
jointVecInit.noalias() = jointPosInit;
for (int i = ava.model.numJoints() - 1; i >= 1; --i) {
jointVecInit.col(i).noalias() -= jointVecInit.col(ava.model.parent[i]);
}
shapeComputed = true;
}
void PrepareForEvaluation(bool evaluate_jacobians,
bool new_evaluation_point) final {
// std::cerr << "PREP " << evaluate_jacobians << ", " << new_evaluation_point << "\n";
if (new_evaluation_point) {
// BEGIN_PROFILE;
if (shapeEnabled || !shapeComputed) CalcShape();
// PROFILE(CalcShape);
jointVecInit.col(0).noalias() = ava.p;
for (int i = 0; i < numJointSpaces - 1; ++i) {
// double * jacobian = localJacobian[i].data();
auto& x = opt.r[i].coeffs();
/** Jacobian of local parameterization '+' function at 0 */
localJacobian[i] << x(3), x(2), -x(1),
-x(2), x(3), x(0),
x(1), -x(0), x(3),
-x(0), -x(1), -x(2);
}
// PROFILE(lojac);
// Compute relative rotations and translations
R(-1, -1).setIdentity(); // -1 means 'to global'
t(-1, -1).setZero();
for (int i = 0; i < numJointSpaces - 1; ++i) {
R(i, i).setIdentity();
Eigen::Matrix3d rot = opt.r[i].toRotationMatrix();
t(i, i).setZero();
int p = ava.model.parent[i];
for (int j = p; ; j = ava.model.parent[j]) {
R(j, i).noalias() = R(j, p) * rot;
t(j, i).noalias() = R(j, p) * jointVecInit.col(i) + t(j, p);
if (j == -1) break;
}
}
// PROFILE(RT);
if (shapeEnabled) {
// Compute joint-to-parent accumulated shape differences
for (int j = 1; j < numJointSpaces - 1; ++j) {
H[j].noalias() = R(-1, ava.model.parent[j]) * Sp[j] + H[ava.model.parent[j]];
}
}
// PROFILE(H);
std::atomic<size_t> cacheId(0);
auto worker = [evaluate_jacobians, &cacheId, this](int workerId) {
size_t workerCacheId;
while (true) {
workerCacheId = cacheId++;
if (workerCacheId >= caches.size()) break;
caches[workerCacheId].updateData(evaluate_jacobians);
}
};
std::vector<std::thread> threadPool;
for (int i = 0; i < numThreads; ++i) {
threadPool.emplace_back(worker, i);
}
for (auto& thread : threadPool) {
thread.join();
}
// PROFILE(Caches);
// PROFILE(ALL Prepare);
}
}
/** Joint-to-ancestor joint rotation (j_ances=-1 is global) */
inline Eigen::Matrix3d& R(int j_ancestor, int j) {
return _R[numJointSpaces * (j_ancestor+1) + j+1];
}
/** Joint-to-ancestor joint relative position (j_ances=-1 is global) */
inline Eigen::Vector3d& t(int j_ancestor, int j) {
return _t[numJointSpaces * (j_ancestor+1) + j+1];
}
/** Combined left-side matrix to multiply into Jacobians for joint j, component t */
// inline Eigen::Matrix3d& L(int j, int t) {
// return _L[j * AvatarOptimizer::ROT_SIZE + t];
// }
AvatarOptimizer& opt;
Avatar& ava;
/** WARNING: is actual number of joints + 1 */
int numJointSpaces;
struct Ancestor {
Ancestor() {}
explicit Ancestor(int jid, int assigned = -1, double assign_weight = 0.0) : jid(jid) {
if (assigned >= 0) {
assign[0] = assigned;
weight[0] = assign_weight;
num_assign = 1;
} else {
num_assign = 0;
}
}
/** Joint ID */
int jid;
/** Assigned joints of the skin point corresponding to the joint */
int assign[MAX_ASSIGN];
/** Assignment weight */
double weight[MAX_ASSIGN];
/** Number of assigned joints. */
int num_assign;
/** Compare by joint id */
bool operator <(const Ancestor& other) const {
return jid < other.jid;
}
/** Merge another ancestor joint */
void merge(const Ancestor& other) {
for (int i = 0; i < other.num_assign; ++i) {
weight[num_assign] = other.weight[i];
assign[num_assign++] = other.assign[i];
}
}
};
/** Max number of threads to use during common pre-evaluation computations */
int numThreads = 1;
/** INTERNAL: Scaled versions of betaPose, betaShape actually
* used in the optimization. This accounts for
* variations in the number of ICP-type residuals. */
double scaledBetaPose, scaledBetaShape;
/** Deduped, topo sorted ancestor joints for each skin point,
* combining all joint assignments for the point */
std::vector<std::vector<Ancestor> > ancestor;
/** Joint initial relative/absolute positions */
CloudType jointVecInit, jointPosInit;
/** baseCloud after applying shape keys (3 * num points) */
CloudType shapedCloud;
/** List of point-specific caches */
std::vector<Cache> caches;
/** Local parameterization jacobian */
std::vector<Eigen::Matrix<double, 4, 3, Eigen::RowMajor>, Eigen::aligned_allocator<Eigen::Matrix<double, 4, 3, Eigen::RowMajor> > > localJacobian;
/** Joint-to-parent 'shape deltas' */
std::vector<CloudType, Eigen::aligned_allocator<CloudType> > S;
/** Joint-to-parent 'shape delta difference' */
std::vector<CloudType, Eigen::aligned_allocator<CloudType> > Sp;
/** Accumulated joint-to-parent 'shape delta difference' */
std::vector<CloudType, Eigen::aligned_allocator<CloudType> > H;
private:
/** Joint-to-ancestor joint rotation (j_ances=-1 is global) */
std::vector<Eigen::Matrix3d, MatAlloc> _R;
/** Joint-to-ancestor joint relative position (j_ances=-1 is global) */
std::vector<Eigen::Vector3d, VecAlloc> _t;
/** Combined left-side matrix to use for Jacobians for joint j, component t */
// std::vector<Eigen::Matrix3d, MatAlloc> _L;
/** True if CalcShape() has been called, to avoid further calls */
bool shapeComputed;
};
/** Common method for each model point */
struct AvatarCostFunctorCache {
AvatarCostFunctorCache(AvatarEvaluationCommonData<AvatarCostFunctorCache>& common_data,
int point_id)
: commonData(common_data), opt(common_data.opt), ava(common_data.opt.ava),
pointId(point_id) {
icpJacobian.resize(commonData.ancestor[pointId].size());
if(commonData.shapeEnabled) {
icpShapeJacobian.resize(3, ava.model.numShapeKeys());
}
}
bool getICPJacobians(double* residuals, double** jacobians) const {
Eigen::Map<Eigen::Vector3d> residualMap(residuals);
residualMap.noalias() = resid;
if (jacobians != nullptr) {
if (jacobians[0] != nullptr) {
Eigen::Map<Eigen::Matrix<double, 3, 3, Eigen::RowMajor>> J(jacobians[0]);
J.setIdentity();
}
size_t i = 0;
for (; i < commonData.ancestor[pointId].size(); ++i) {
if (jacobians[i+1] != nullptr) {
Eigen::Map<Eigen::Matrix<double, 3, 4, Eigen::RowMajor> > J(jacobians[i+1]);
#ifdef TEST_COMPARE_AUTO_DIFF
J.noalias() = icpJacobian[i];
#else
J.topLeftCorner<3,3>().noalias() = icpJacobian[i];
J.rightCols<1>().setZero();
#endif
}
}
if (commonData.shapeEnabled && jacobians[i + 1] != nullptr) {
Eigen::Map<Eigen::Matrix<double, 3, Eigen::Dynamic, Eigen::RowMajor> > J(jacobians[i + 1], 3, ava.model.numShapeKeys());
J.noalias() = icpShapeJacobian;
}
}
return true;
}
void updateData(bool compute_jacobians) {
auto pointPosInit = commonData.shapedCloud.col(pointId);
resid.setZero();
for (auto& assign : ava.model.assignedJoints[pointId]) {
int k = assign.second;
resid += assign.first * (commonData.R(-1, k) * (pointPosInit - commonData.jointPosInit.col(k)) + commonData.t(-1, k));
}
if (compute_jacobians) {
// Root position derivative is always identity
// TODO: precompute point-to-assigned-joint vector, reduce 3 flops
//CloudType pointVecs;
Eigen::Matrix<double, 3, 4> dRot;
// Eigen::Matrix3d vCross;
Eigen::Vector3d v;
for (size_t i = 0; i < commonData.ancestor[pointId].size(); ++i) {
// Set derivative for each parent rotation
auto& ances = commonData.ancestor[pointId][i];
int j = ances.jid; // 'middle' joint we are differenting wrt
v.setZero();
for (int assign = 0; assign < ances.num_assign; ++assign) {
// up to 4 inner loops
int k = ances.assign[assign]; // 'outer' joint assigned to the point
v += ances.weight[assign]* (commonData.R(j, k) *
(pointPosInit - commonData.jointPosInit.col(k)) + commonData.t(j, k));
}
// std::cerr <<v.transpose<< "\n"
Eigen::Quaterniond& q = opt.r[j];
Eigen::Vector3d u = q.vec() * 2;
// Quaternion-vector rotation (pseudo-)Jacobian
double w = q.w() * 2;
dRot.setZero();
dRot <<
u(1)*v(1) + v(2)*u(2) ,
w*v(2) + u(0)*v(1) - 2*u(1)*v(0),
-w*v(1) - 2*v(0)*u(2) + u(0)*v(2),
u(1)*v(2) - v(1)*u(2),
-w*v(2) - 2*u(0)*v(1) + v(0)*u(1),
v(2)*u(2) + u(0)*v(0),
w*v(0) + u(1)*v(2) - 2*v(1)*u(2),
v(0)*u(2) - u(0)*v(2),
w*v(1) + v(0)*u(2) - 2*u(0)*v(2),
-w*v(0) - 2*u(1)*v(2) + v(1)*u(2),
u(0)*v(0) + v(1)*u(1),
u(0)*v(1) - v(0)*u(1);
icpJacobian[i].setZero();
icpJacobian[i].noalias() = commonData.R(-1, ava.model.parent[j]) * dRot
#ifndef TEST_COMPARE_AUTO_DIFF
* commonData.localJacobian[j]
#endif
;
}
if (commonData.shapeEnabled) {
icpShapeJacobian.setZero();
for (const std::pair<double,int>& assign : ava.model.assignedJoints[pointId]) {
const int j = assign.second;
auto pointDeltas = ava.model.keyClouds.middleRows<3>(pointId * 3);
icpShapeJacobian += (commonData.R(-1, j) * (pointDeltas - commonData.S[j]) + commonData.H[j]) * assign.first;
}
}
}
}
Eigen::Vector3d resid;
#ifdef TEST_COMPARE_AUTO_DIFF
// For comparing with auto diff, we cannot multiply by the
// local param jacobian or result would not be comparable
std::vector<Eigen::Matrix<double, 3, 4, Eigen::RowMajor>,
Eigen::aligned_allocator<
Eigen::Matrix<double, 3, 4, Eigen::RowMajor> > > icpJacobian;
#else
std::vector<Eigen::Matrix<double, 3, 3, Eigen::RowMajor>,
Eigen::aligned_allocator<
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> > > icpJacobian;
#endif
Eigen::Matrix<double, 3, Eigen::Dynamic, Eigen::RowMajor> icpShapeJacobian;
Avatar& ava;
AvatarOptimizer& opt;
AvatarEvaluationCommonData<AvatarCostFunctorCache>& commonData;
int pointId;
};
/** Ceres analytic derivative cost function for ICP error.
* Works for pose parameters only. */
struct AvatarICPCostFunctor : ceres::CostFunction {
AvatarICPCostFunctor(AvatarEvaluationCommonData<AvatarCostFunctorCache> & common_data,
size_t cache_id, const CloudType& data_cloud, int data_point_id)
: commonData(common_data), cacheId(cache_id), dataCloud(data_cloud), dataPointId(data_point_id) {
set_num_residuals(3);
auto& cache = common_data.caches[cache_id];
std::vector<int> * paramBlockSizes = mutable_parameter_block_sizes();
paramBlockSizes->push_back(3); // Root position
for (size_t i = 0; i < cache.commonData.ancestor[cache.pointId].size(); ++i) {
paramBlockSizes->push_back(4); // Add rotation block for each ancestor
}
if (commonData.shapeEnabled) paramBlockSizes->push_back(cache.ava.model.numShapeKeys()); // Shape key weights?
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
if (!commonData.caches[cacheId].getICPJacobians(residuals, jacobians)) return false;
Eigen::Map<Eigen::Vector3d> resid(residuals);
resid -= dataCloud.col(dataPointId);
return true;
}
const CloudType& dataCloud;
int dataPointId;
size_t cacheId;
AvatarEvaluationCommonData<AvatarCostFunctorCache>& commonData;
};
/** Ceres analytic derivative cost function for pose prior error */
struct AvatarPosePriorCostFunctor : ceres::CostFunction {
AvatarPosePriorCostFunctor(AvatarEvaluationCommonData<AvatarCostFunctorCache> & common_data)
: commonData(common_data), posePrior(commonData.ava.model.posePrior),
nSmplJoints(commonData.ava.model.numJoints() - 1) {
set_num_residuals(nSmplJoints * 3 + 1); // 3 for each joint + 1 extra
std::vector<int> * paramBlockSizes = mutable_parameter_block_sizes();
for (int i = 0; i < nSmplJoints; ++i) {
paramBlockSizes->push_back(4); // Add rotation block for each non-root joint
}
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
const int nResids = nSmplJoints * 3 + 1;
Eigen::VectorXd smplParams(nSmplJoints * 3);
for (int i = 0; i < nSmplJoints; ++i) {
Eigen::Map<const Eigen::Quaterniond> q(parameters[i]);
Eigen::AngleAxisd aa(q);
smplParams.segment<3>(i * 3) = aa.axis() * aa.angle();
}
Eigen::Map<Eigen::VectorXd> resid(residuals, nResids);
int compIdx;
resid.noalias() = posePrior.residual(smplParams, &compIdx) * commonData.scaledBetaPose;
if (jacobians != nullptr) {
const Eigen::MatrixXd& L = posePrior.prec_cho[compIdx];
// precision = L L^T
for (int i = 0; i < nSmplJoints; ++i) {
if (jacobians[i] != nullptr) {
Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, 4, Eigen::RowMajor> > J(jacobians[i], nResids, 4);
J.topLeftCorner<Eigen::Dynamic, 3>(nResids - 1, 3).noalias() = L.middleRows<3>(i * 3).transpose() *
0.707106781186548 * commonData.scaledBetaPose;
J.rightCols<1>().setZero();
J.bottomLeftCorner<1, 3>().setZero();
}
}
}
return true;
}
AvatarEvaluationCommonData<AvatarCostFunctorCache>& commonData;
const GaussianMixture& posePrior;
const int nSmplJoints;
};
/** Ceres analytic derivative cost function for shape prior error
* (This is extremely simple, just the squared l2-norm of w!) */
struct AvatarShapePriorCostFunctor : ceres::CostFunction {
AvatarShapePriorCostFunctor(int num_shape_keys, double beta_shape) :
numShapeKeys(num_shape_keys), betaShape(beta_shape) {
set_num_residuals(numShapeKeys); // 1 for each shape key
std::vector<int> * paramBlockSizes = mutable_parameter_block_sizes();
paramBlockSizes->push_back(numShapeKeys); // 1 for each shape key
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
Eigen::Map<Eigen::VectorXd> resid(residuals, numShapeKeys);
Eigen::Map<const Eigen::VectorXd> w(parameters[0], numShapeKeys);
resid.noalias() = w * betaShape;
if (jacobians != nullptr) {
if (jacobians[0] != nullptr) {
Eigen::Map<Eigen::MatrixXd> J(jacobians[0], numShapeKeys, numShapeKeys);
J.noalias() = Eigen::MatrixXd::Identity(numShapeKeys, numShapeKeys) * betaShape;
}
}
return true;
}
const int numShapeKeys;
const double betaShape;
};
#ifdef TEST_COMPARE_AUTO_DIFF
/** Auto diff cost function w/ derivative for Ceres
* (Extremely poorly optimized, used for checking correctness of analytic derivative) */
struct AvatarICPAutoDiffCostFunctor {
AvatarICPAutoDiffCostFunctor(AvatarEvaluationCommonData<AvatarCostFunctorCache> & common_data,
size_t cache_id, const CloudType& data_cloud, int data_point_id)
: commonData(common_data), cacheId(cache_id), dataCloud(data_cloud), dataPointId(data_point_id) {
pointId = commonData.caches[cacheId].pointId;
}
template<class T>
bool operator()(T const* const* params, T* residual) const {
using VecMap = Eigen::Map<Eigen::Matrix<T, 3, 1> >;
using ConstVecMap = Eigen::Map<const Eigen::Matrix<T, 3, 1> >;
using ConstQuatMap = Eigen::Map<const Eigen::Quaternion<T> >;
Eigen::Matrix<T, 3, Eigen::Dynamic> cloud(3, commonData.ava.model.numPoints());
Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, 1> > cloudVec(cloud.data(), cloud.rows() * cloud.cols());
if (commonData.shapeEnabled) {
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1> > wMap(params[commonData.ava.model.numJoints() + 1], commonData.ava.model.numShapeKeys(), 1);
cloudVec.noalias() = commonData.ava.model.keyClouds.cast<T>() * wMap + commonData.ava.model.baseCloud;
} else {
cloudVec.noalias() = commonData.ava.model.keyClouds.cast<T>() * commonData.ava.w.cast<T>() + commonData.ava.model.baseCloud;
}
Eigen::Matrix<T, 3, Eigen::Dynamic> jointPos =
cloud * commonData.ava.model.jointRegressor.cast<T>();
if (commonData.ava.model.useJointShapeRegressor) {
jointPos.resize(3, commonData.ava.model.numJoints());
Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, 1> > jointPosVec(jointPos.data(), 3 * commonData.ava.model.numJoints());
if (commonData.shapeEnabled) {
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1> > wMap(params[commonData.ava.model.numJoints() + 1], commonData.ava.model.numShapeKeys(), 1);
jointPosVec.noalias() = commonData.ava.model.jointShapeRegBase.cast<T>() + commonData.ava.model.jointShapeReg.cast<T>() * wMap;
} else {
jointPosVec.noalias() = commonData.ava.model.jointShapeRegBase.cast<T>() + commonData.ava.model.jointShapeReg.cast<T>() * commonData.ava.w.cast<T>();
}
} else {
jointPos.noalias() = cloud * commonData.ava.model.jointRegressor.cast<T>();
}
Eigen::Matrix<T, 3, 1> offset = jointPos.col(0);
cloud.colwise() -= offset;
jointPos.colwise() -= offset;
VecMap resid(residual);
resid.setZero();
ConstVecMap rootPos(params[0]);
for (auto& assign: commonData.ava.model.assignedJoints[pointId]) {
Eigen::Matrix<T, 3, 1> vec = (cloud.col(pointId) - jointPos.col(assign.second)).template cast<T>();
for (int p = assign.second; p != -1; p = commonData.ava.model.parent[p]) {
vec = ConstQuatMap(params[p+1]).toRotationMatrix() * vec;
// Do not add if root joint since we want to add the rootPos
// instead in autodiff case
if (p) vec.noalias() += jointPos.col(p) - jointPos.col(commonData.ava.model.parent[p]);
}
resid.noalias() += assign.first * vec;
}
resid.noalias() += rootPos;
resid.noalias() -= dataCloud.col(dataPointId);
return true;
}
const CloudType& dataCloud;
int dataPointId, pointId;
size_t cacheId;
AvatarEvaluationCommonData<AvatarCostFunctorCache>& commonData;
};
#endif // TEST_COMPARE_AUTO_DIFF
typedef nanoflann::KDTreeEigenColMajorMatrixAdaptor<
CloudType, 3, nanoflann::metric_L2_Simple> KdTree;
void findNN(const CloudType & data_cloud,
const Eigen::VectorXi& data_part_labels,
const std::vector<Eigen::VectorXi>& data_part_indices,
const CloudType & model_cloud,
const Eigen::VectorXi& model_part_labels,
const std::vector<Eigen::VectorXi>& model_part_indices,
std::vector<CloudType>& model_part_clouds,
std::vector<bool>& point_visible,
std::vector<std::vector<int> > & correspondences,
std::vector<std::unique_ptr<KdTree>>& part_kd,
int nn_step,
int num_threads,
bool invert = false) {
if (invert) {
size_t index; double dist;
nanoflann::KNNResultSet<double> resultSet(1);
// match each data point to a model point
typedef nanoflann::KDTreeEigenColMajorMatrixAdaptor<
CloudType, 3, nanoflann::metric_L2_Simple> KdTree;
const size_t numParts = model_part_indices.size();
std::vector<KdTree*> modelPartKD(numParts);
std::vector<std::vector<int>> newModelPartIndices(numParts);
{
std::atomic<int> part(0);
auto worker = [&]() {
int i = 0;
while (true) {
i = part++;
if (i >= numParts) break;
const auto& indices = model_part_indices[i];
int cnt = 0;
for (int j = 0; j < indices.rows(); ++j) {
int k = indices[j];
if (point_visible[k]) ++cnt;
}
auto& partCloud = model_part_clouds[i];
if (cnt == 0) {
modelPartKD[i] = nullptr;
continue;
}
partCloud.resize(3, cnt);
cnt = 0;
for (int j = 0; j < indices.rows(); ++j) {
int k = indices[j];
if (!point_visible[k]) continue;
partCloud.col(cnt++).noalias() = model_cloud.col(k);
newModelPartIndices[i].push_back(k);
}
modelPartKD[i] = new KdTree(model_part_clouds[i], 10);
modelPartKD[i]->index->buildIndex();
}
};
std::vector<std::thread> thds;
for (int i = 0; i < num_threads; ++i) {
thds.emplace_back(worker);
}
for (int i = 0; i < num_threads; ++i) {
thds[i].join();
}
}
correspondences.resize(model_cloud.cols());
for (int i = 0; i < model_cloud.cols(); ++i) {
correspondences[i].clear();
}
for (int i = 0; i < data_cloud.cols(); ++i) {
resultSet.init(&index, &dist);
const int partId = data_part_labels[i];
if (modelPartKD[partId] == nullptr) continue;
modelPartKD[partId]
->index->findNeighbors(
resultSet,
data_cloud.data() + i * 3,
nanoflann::SearchParams(10));
correspondences[
newModelPartIndices[partId][index]
].push_back(i);
}
for (int i = 0; i < numParts; ++i) {
delete modelPartKD[i];
}
// const int MAX_CORRES_PER_POINT = 1;
// for (int i = 0; i < model_cloud.cols(); ++i) {
// if (correspondences[i].size() > MAX_CORRES_PER_POINT) {
// //std::cerr << correspondences[i].size() << "SZ\n";
// for (int j = 0; j < MAX_CORRES_PER_POINT; ++j) {
// int r = random_util::randint<int>(j, correspondences[i].size()-1);
// std::swap(correspondences[i][j], correspondences[i][r]);
// }
// correspondences[i].resize(MAX_CORRES_PER_POINT);
// }
// }
} else {
size_t index; double dist;
nanoflann::KNNResultSet<double> resultSet(1);
// match each model point to a data point
correspondences.resize(model_cloud.cols());
for (int i = 0; i < model_cloud.cols(); ++i) {
correspondences[i].clear();
}
Eigen::VectorXi perPart(part_kd.size());
perPart.setZero();
for (int i = 0; i < model_cloud.cols(); i += nn_step) {
if (!point_visible[i]) continue;
resultSet.init(&index, &dist);
int partId = model_part_labels[i];
auto* kd_tree = part_kd[partId].get();
if (kd_tree) {
kd_tree->index->findNeighbors(resultSet, model_cloud.data() + i * 3, nanoflann::SearchParams(10));
correspondences[i].push_back(data_part_indices[partId][index]);
++perPart[partId];
}
}
for (int i = 0; i < perPart.rows(); ++i){
std::cout << perPart(i) << " ";
}
std::cout << "!!\n";
// if (ownTree) {
// delete kd_tree;
// }
/*
const int MAX_CORRES_PER_POINT = 200;
for (int i = 0; i < model_cloud.cols(); ++i) {
if (correspondences[i].size() > MAX_CORRES_PER_POINT) {
//std::cerr << correspondences[i].size() << "SZ\n";
for (int j = 0; j < MAX_CORRES_PER_POINT; ++j) {
int r = random_util::randint<int>(j, correspondences[i].size()-1);
std::swap(correspondences[i][j], correspondences[i][r]);
}
correspondences[i].resize(MAX_CORRES_PER_POINT);
}
}
*/
}
}
#ifdef PCL_DEBUG_VISUALIZE
void debugVisualize(const pcl::visualization::PCLVisualizer::Ptr& viewer,
const CloudType& data_cloud, std::vector<std::vector<int> > correspondences,
const std::vector<bool>& point_visible, AvatarEvaluationCommonData<AvatarCostFunctorCache>& common) {
auto modelPclCloud = pcl::PointCloud<pcl::PointXYZRGBA>::Ptr(new pcl::PointCloud<pcl::PointXYZRGBA>());
auto dataPclCloud = pcl::PointCloud<pcl::PointXYZRGBA>::Ptr(new pcl::PointCloud<pcl::PointXYZRGBA>());
// auto matchedModelPointsCloud = pcl::PointCloud<pcl::PointXYZRGBA>::Ptr(new pcl::PointCloud<pcl::PointXYZRGBA>());
modelPclCloud->reserve(common.ava.cloud.cols());
for (int i = 0 ; i < common.ava.cloud.cols(); ++i) {
pcl::PointXYZRGBA pt;
pt.getVector3fMap() = common.ava.cloud.col(i).cast<float>();
if (!point_visible[i]) {
// pt.r = pt.g = pt.b = 100;
continue;
} else {
pt.r = 255;
pt.g = 0;
pt.b = 0;
}
pt.a = 255;
modelPclCloud->push_back(std::move(pt));
}
dataPclCloud->reserve(data_cloud.cols());
for (int i = 0 ; i < data_cloud.cols(); ++i) {
pcl::PointXYZRGBA pt;
pt.getVector3fMap() = data_cloud.col(i).cast<float>();
pt.r = 100;
pt.g = 100;
pt.b = 100;
pt.a = 200;
dataPclCloud->push_back(std::move(pt));
}
// matchedModelPointsCloud->reserve(common.caches.size());
// common.PrepareForEvaluation(true, true);
// for (auto& cache : common.caches) {
// cache.updateData(false);
// pcl::PointXYZRGBA pt;
// pt.getVector3fMap() = cache.resid.cast<float>();
// pt.r = 0;
// pt.g = 255;
// pt.b = 0;
// pt.a = 255;
// matchedModelPointsCloud->push_back(std::move(pt));
// }
viewer->setBackgroundColor(0, 0, 0);
viewer->removePointCloud("cloud");
viewer->removePointCloud("cloudData");
viewer->removePointCloud("cachesCloud");
viewer->removeAllShapes();
viewer->addPointCloud(modelPclCloud, "cloud", 0);
viewer->addPointCloud(dataPclCloud, "cloudData", 0);
// viewer->addPointCloud(matchedModelPointsCloud, "cachesCloud", 0);
/*
for (int i = 0; i < common.ava.model.numJoints(); ++i) {
pcl::PointXYZRGBA curr;
curr.x = common.jointPosInit(0, i);
curr.y = common.jointPosInit(1, i);
curr.z = common.jointPosInit(2, i);
//std::cerr << "Joint:" << joints[i]->name << ":" << curr.x << "," << curr.y << "," << curr.z << "\n";
cv::Vec3f colorf(0.f, 0.f, 1.0f);
std::string jointName = "avatarJoint" + std::to_string(i);
viewer->removeShape(jointName, 0);
viewer->addSphere(curr, 0.02, colorf[0], colorf[1], colorf[2], jointName, 0);
// if (joints[i]->parent) {
// p parent = util::toPCLPoint(joints[i]->parent->posTransformed);
// std::string boneName = pcl_prefix + "avatarBone" + std::to_string(i);
// viewer->removeShape(boneName, viewport);
// viewer->addLine(curr, parent, colorf[2], colorf[1], colorf[0], boneName, viewport);
// }
}
*/
for (size_t i = 0; i < correspondences.size(); ++i) {
for (size_t j = 0; j < correspondences[i].size(); ++j) {
// if (random_util::uniform(0.0, 1.0) > 0.05) continue;
pcl::PointXYZ p1, p2;
p1.getVector3fMap() = common.ava.cloud.col(i).cast<float>();
p2.getVector3fMap() = data_cloud.col(correspondences[i][j]).cast<float>();
std::string name = "nn_line_" + std::to_string(i) +"_" + std::to_string(correspondences[i][j]);
viewer->addLine<pcl::PointXYZ, pcl::PointXYZ>(p2, p1, 0.0, 1.0, 0.0, name, 0);
}
}
viewer->spin();
}
#endif
#ifdef TEST_COMPARE_AUTO_DIFF
/** Given a model point-data point pair, this function checks that autodiff gives the same result
* as the user-supplied analytic derivatives. Useful for verifying correctness. */
void testCompareAutoDiff(AvatarOptimizer& opt, const ark::CloudType& data_cloud, int model_point_id, int data_point_id) {
using namespace ceres;
std::cerr << "INFO: COMPARING AUTO DIFF\n";
std::cerr << data_cloud.col(data_point_id).transpose() << " DATA POINT\n";
AvatarEvaluation1ommonData<AvatarCostFunctorCache> common(opt, true);
common.scaledBetaShape = opt.betaShape;
common.scaledBetaPose = opt.betaPose;
std::cerr << common.shapedCloud.col(model_point_id).transpose() << " MODEL POINT, init\n";
for (auto& ances : common.ancestor[model_point_id]) {
std::cerr << ances.jid << ", p() = " << opt.ava.model.parent[ances.jid] << "\n";
std::cerr << common.jointPosInit.col(ances.jid).transpose() << " t\n";
std::cerr << opt.r[ances.jid].w() << ": " << opt.r[ances.jid].vec().transpose() << " q\n";
std::cerr << "\n";
}
std::cerr << "\n";
for (auto&assign : opt.ava.model.assignedJoints[model_point_id]) {
std::cerr << assign.first <<":" << assign.second << " ";
}
std::cerr << "\n";
common.caches.emplace_back(common, model_point_id);
std::cerr << "Created dummy caches\n";
DynamicAutoDiffCostFunction<AvatarICPAutoDiffCostFunctor>* cost_function = new DynamicAutoDiffCostFunction<AvatarICPAutoDiffCostFunctor>(
new AvatarICPAutoDiffCostFunctor(common, 0, data_cloud, data_point_id));
cost_function->AddParameterBlock(3);
for (int k = 0; k < opt.ava.model.numJoints(); ++k) {
cost_function->AddParameterBlock(4);
}
if (common.shapeEnabled) {
cost_function->AddParameterBlock(opt.ava.model.numShapeKeys());
}
cost_function->SetNumResiduals(3);
AvatarICPCostFunctor* our_cost_function = new AvatarICPCostFunctor(common, 0, data_cloud, data_point_id);
// NULL, pointParams[i]);
std::vector<double*> params, fullParams;
params.reserve(common.ancestor[model_point_id].size() + 1);
params.push_back(common.ava.p.data());
for (auto& ances : common.ancestor[model_point_id]) {
params.push_back(opt.r[ances.jid].coeffs().data());