57 PCL_ERROR (
"[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
61 if (search_radius_< min_radius_)
63 PCL_ERROR (
"[pcl::%s::initCompute] search_radius_ must be GREATER than min_radius_.\n", getClassName ().c_str ());
68 descriptor_length_ = elevation_bins_ * azimuth_bins_ * radius_bins_;
71 float azimuth_interval = 360.0f /
static_cast<float> (azimuth_bins_);
72 float elevation_interval = 180.0f /
static_cast<float> (elevation_bins_);
75 radii_interval_.clear ();
76 phi_divisions_.clear ();
77 theta_divisions_.clear ();
81 radii_interval_.resize (radius_bins_ + 1);
82 for (std::size_t j = 0; j < radius_bins_ + 1; j++)
83 radii_interval_[j] =
static_cast<float> (std::exp (std::log (min_radius_) + ((
static_cast<float> (j) /
static_cast<float> (radius_bins_)) * std::log (search_radius_ / min_radius_))));
86 theta_divisions_.resize (elevation_bins_ + 1, elevation_interval);
87 theta_divisions_[0] = 0.f;
88 std::partial_sum(theta_divisions_.begin (), theta_divisions_.end (), theta_divisions_.begin ());
91 phi_divisions_.resize (azimuth_bins_ + 1, azimuth_interval);
92 phi_divisions_[0] = 0.f;
93 std::partial_sum(phi_divisions_.begin (), phi_divisions_.end (), phi_divisions_.begin ());
100 float e = 1.0f / 3.0f;
102 volume_lut_.resize (radius_bins_ * elevation_bins_ * azimuth_bins_);
104 for (std::size_t j = 0; j < radius_bins_; j++)
107 float integr_r = (radii_interval_[j+1] * radii_interval_[j+1] * radii_interval_[j+1] / 3.0f) - (radii_interval_[j] * radii_interval_[j] * radii_interval_[j] / 3.0f);
109 for (std::size_t k = 0; k < elevation_bins_; k++)
112 float integr_theta = std::cos (
pcl::deg2rad (theta_divisions_[k])) - std::cos (
pcl::deg2rad (theta_divisions_[k+1]));
114 float V = integr_phi * integr_theta * integr_r;
120 for (std::size_t l = 0; l < azimuth_bins_; l++)
124 volume_lut_[(l*elevation_bins_*radius_bins_) + k*radius_bins_ + j] = 1.0f / powf (V, e);
137 Eigen::Map<Eigen::Vector3f> x_axis (rf);
138 Eigen::Map<Eigen::Vector3f> y_axis (rf + 3);
139 Eigen::Map<Eigen::Vector3f> normal (rf + 6);
143 std::vector<float> nn_dists;
144 const std::size_t neighb_cnt = searchForNeighbors ((*indices_)[index], search_radius_, nn_indices, nn_dists);
147 std::fill (desc.begin (), desc.end (), std::numeric_limits<float>::quiet_NaN ());
148 std::fill_n (rf, 9, 0.f);
152 const auto minDistanceIt = std::min_element(nn_dists.begin (), nn_dists.end ());
153 const auto minIndex = nn_indices[std::distance (nn_dists.begin (), minDistanceIt)];
161 std::fill (desc.begin (), desc.end (), std::numeric_limits<float>::quiet_NaN ());
162 std::fill (rf, rf + 9, 0.f);
165 normal = normals[minIndex].getNormalVector3fMap ();
172 x_axis[2] = - (normal[0]*x_axis[0] + normal[1]*x_axis[1]) / normal[2];
174 x_axis[1] = - (normal[0]*x_axis[0] + normal[2]*x_axis[2]) / normal[1];
176 x_axis[0] = - (normal[1]*x_axis[1] + normal[2]*x_axis[2]) / normal[0];
181 assert (
pcl::utils::equal (x_axis[0]*normal[0] + x_axis[1]*normal[1] + x_axis[2]*normal[2], 0.0f, 1E-6f));
184 y_axis.matrix () = normal.cross (x_axis);
187 for (std::size_t ne = 0; ne < neighb_cnt; ne++)
192 Eigen::Vector3f neighbour = (*surface_)[nn_indices[ne]].getVector3fMap ();
196 float r = std::sqrt (nn_dists[ne]);
199 Eigen::Vector3f proj;
207 Eigen::Vector3f cross = x_axis.cross (proj);
208 float phi =
pcl::rad2deg (std::atan2 (cross.norm (), x_axis.dot (proj)));
209 phi = cross.dot (normal) < 0.f ? (360.0f - phi) : phi;
211 Eigen::Vector3f no = neighbour - origin;
213 float theta = normal.dot (no);
214 theta =
pcl::rad2deg (std::acos (std::min (1.0f, std::max (-1.0f, theta))));
217 const auto rad_min = std::lower_bound(std::next (radii_interval_.cbegin ()), radii_interval_.cend (), r);
218 const auto theta_min = std::lower_bound(std::next (theta_divisions_.cbegin ()), theta_divisions_.cend (), theta);
219 const auto phi_min = std::lower_bound(std::next (phi_divisions_.cbegin ()), phi_divisions_.cend (), phi);
222 const auto j = std::distance(radii_interval_.cbegin (), std::prev(rad_min));
223 const auto k = std::distance(theta_divisions_.cbegin (), std::prev(theta_min));
224 const auto l = std::distance(phi_divisions_.cbegin (), std::prev(phi_min));
228 std::vector<float> neighbour_distances;
229 int point_density = searchForNeighbors (*surface_, nn_indices[ne], point_density_radius_, neighbour_indices, neighbour_distances);
231 if (point_density == 0)
234 float w = (1.0f /
static_cast<float> (point_density)) *
235 volume_lut_[(l*elevation_bins_*radius_bins_) + (k*radius_bins_) + j];
238 if (w == std::numeric_limits<float>::infinity ())
239 PCL_ERROR (
"Shape Context Error INF!\n");
241 PCL_ERROR (
"Shape Context Error IND!\n");
243 desc[(l*elevation_bins_*radius_bins_) + (k*radius_bins_) + j] += w;
245 assert (desc[(l*elevation_bins_*radius_bins_) + (k*radius_bins_) + j] >= 0);
249 std::fill_n (rf, 9, 0);
257 assert (descriptor_length_ == 1980);
259 output.is_dense =
true;
261 for (std::size_t point_index = 0; point_index < indices_->size (); point_index++)
266 if (!
isFinite ((*input_)[(*indices_)[point_index]]))
268 std::fill_n (output[point_index].descriptor, descriptor_length_,
269 std::numeric_limits<float>::quiet_NaN ());
270 std::fill_n (output[point_index].rf, 9, 0);
271 output.is_dense =
false;
275 std::vector<float> descriptor (descriptor_length_);
276 if (!computePoint (point_index, *normals_, output[point_index].rf, descriptor))
277 output.is_dense =
false;
278 std::copy (descriptor.cbegin (), descriptor.cend (), output[point_index].descriptor);
bool computePoint(std::size_t index, const pcl::PointCloud< PointNT > &normals, float rf[9], std::vector< float > &desc)
Estimate a descriptor for a given point.
bool initCompute() override
Initialize computation by allocating all the intervals and the volume lookup table.
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
void computeFeature(PointCloudOut &output) override
Estimate the actual feature.