Point Cloud Library (PCL)  1.8.1-dev
sift_keypoint.hpp
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37 
38 #ifndef PCL_SIFT_KEYPOINT_IMPL_H_
39 #define PCL_SIFT_KEYPOINT_IMPL_H_
40 
41 #include <pcl/keypoints/sift_keypoint.h>
42 #include <pcl/common/io.h>
43 #include <pcl/filters/voxel_grid.h>
44 
45 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
46 template <typename PointInT, typename PointOutT> void
47 pcl::SIFTKeypoint<PointInT, PointOutT>::setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
48 {
49  min_scale_ = min_scale;
50  nr_octaves_ = nr_octaves;
51  nr_scales_per_octave_ = nr_scales_per_octave;
52 }
53 
54 
55 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
56 template <typename PointInT, typename PointOutT> void
58 {
59  min_contrast_ = min_contrast;
60 }
61 
62 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
63 template <typename PointInT, typename PointOutT> bool
65 {
66  if (min_scale_ <= 0)
67  {
68  PCL_ERROR ("[pcl::%s::initCompute] : Minimum scale (%f) must be strict positive!\n",
69  name_.c_str (), min_scale_);
70  return (false);
71  }
72  if (nr_octaves_ < 1)
73  {
74  PCL_ERROR ("[pcl::%s::initCompute] : Number of octaves (%d) must be at least 1!\n",
75  name_.c_str (), nr_octaves_);
76  return (false);
77  }
78  if (nr_scales_per_octave_ < 1)
79  {
80  PCL_ERROR ("[pcl::%s::initCompute] : Number of scales per octave (%d) must be at least 1!\n",
81  name_.c_str (), nr_scales_per_octave_);
82  return (false);
83  }
84  if (min_contrast_ < 0)
85  {
86  PCL_ERROR ("[pcl::%s::initCompute] : Minimum contrast (%f) must be non-negative!\n",
87  name_.c_str (), min_contrast_);
88  return (false);
89  }
90 
91  this->setKSearch (1);
92  tree_.reset (new pcl::search::KdTree<PointInT> (true));
93  return (true);
94 }
95 
96 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
97 template <typename PointInT, typename PointOutT> void
99 {
100  if (surface_ && surface_ != input_)
101  {
102  PCL_WARN ("[pcl::%s::detectKeypoints] : ", name_.c_str ());
103  PCL_WARN ("A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does ");
104  PCL_WARN ("not support search surfaces other than the input cloud. ");
105  PCL_WARN ("The cloud provided in setInputCloud is being used instead.\n");
106  }
107 
108  // Check if the output has a "scale" field
109  scale_idx_ = pcl::getFieldIndex<PointOutT> (output, "scale", out_fields_);
110 
111  // Make sure the output cloud is empty
112  output.points.clear ();
113 
114  // Create a local copy of the input cloud that will be resized for each octave
115  boost::shared_ptr<pcl::PointCloud<PointInT> > cloud (new pcl::PointCloud<PointInT> (*input_));
116 
117  VoxelGrid<PointInT> voxel_grid;
118  // Search for keypoints at each octave
119  float scale = min_scale_;
120  for (int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
121  {
122  // Downsample the point cloud
123  const float s = 1.0f * scale; // note: this can be adjusted
124  voxel_grid.setLeafSize (s, s, s);
125  voxel_grid.setInputCloud (cloud);
126  boost::shared_ptr<pcl::PointCloud<PointInT> > temp (new pcl::PointCloud<PointInT>);
127  voxel_grid.filter (*temp);
128  cloud = temp;
129 
130  // Make sure the downsampled cloud still has enough points
131  const size_t min_nr_points = 25;
132  if (cloud->points.size () < min_nr_points)
133  break;
134 
135  // Update the KdTree with the downsampled points
136  tree_->setInputCloud (cloud);
137 
138  // Detect keypoints for the current scale
139  detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output);
140 
141  // Increase the scale by another octave
142  scale *= 2;
143  }
144 
145  // Set final properties
146  output.height = 1;
147  output.width = static_cast<uint32_t> (output.points.size ());
148  output.header = input_->header;
149  output.sensor_origin_ = input_->sensor_origin_;
150  output.sensor_orientation_ = input_->sensor_orientation_;
151 }
152 
153 
154 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
155 template <typename PointInT, typename PointOutT> void
157  const PointCloudIn &input, KdTree &tree, float base_scale, int nr_scales_per_octave,
158  PointCloudOut &output)
159 {
160  // Compute the difference of Gaussians (DoG) scale space
161  std::vector<float> scales (nr_scales_per_octave + 3);
162  for (int i_scale = 0; i_scale <= nr_scales_per_octave + 2; ++i_scale)
163  {
164  scales[i_scale] = base_scale * powf (2.0f, (1.0f * static_cast<float> (i_scale) - 1.0f) / static_cast<float> (nr_scales_per_octave));
165  }
166  Eigen::MatrixXf diff_of_gauss;
167  computeScaleSpace (input, tree, scales, diff_of_gauss);
168 
169  // Find extrema in the DoG scale space
170  std::vector<int> extrema_indices, extrema_scales;
171  findScaleSpaceExtrema (input, tree, diff_of_gauss, extrema_indices, extrema_scales);
172 
173  output.points.reserve (output.points.size () + extrema_indices.size ());
174  // Save scale?
175  if (scale_idx_ != -1)
176  {
177  // Add keypoints to output
178  for (size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
179  {
180  PointOutT keypoint;
181  const int &keypoint_index = extrema_indices[i_keypoint];
182 
183  keypoint.x = input.points[keypoint_index].x;
184  keypoint.y = input.points[keypoint_index].y;
185  keypoint.z = input.points[keypoint_index].z;
186  memcpy (reinterpret_cast<char*> (&keypoint) + out_fields_[scale_idx_].offset,
187  &scales[extrema_scales[i_keypoint]], sizeof (float));
188  output.points.push_back (keypoint);
189  }
190  }
191  else
192  {
193  // Add keypoints to output
194  for (size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
195  {
196  PointOutT keypoint;
197  const int &keypoint_index = extrema_indices[i_keypoint];
198 
199  keypoint.x = input.points[keypoint_index].x;
200  keypoint.y = input.points[keypoint_index].y;
201  keypoint.z = input.points[keypoint_index].z;
202 
203  output.points.push_back (keypoint);
204  }
205  }
206 }
207 
208 
209 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
210 template <typename PointInT, typename PointOutT>
212  const PointCloudIn &input, KdTree &tree, const std::vector<float> &scales,
213  Eigen::MatrixXf &diff_of_gauss)
214 {
215  diff_of_gauss.resize (input.size (), scales.size () - 1);
216 
217  // For efficiency, we will only filter over points within 3 standard deviations
218  const float max_radius = 3.0f * scales.back ();
219 
220  for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
221  {
222  std::vector<int> nn_indices;
223  std::vector<float> nn_dist;
224  tree.radiusSearch (i_point, max_radius, nn_indices, nn_dist); // *
225  // * note: at this stage of the algorithm, we must find all points within a radius defined by the maximum scale,
226  // regardless of the configurable search method specified by the user, so we directly employ tree.radiusSearch
227  // here instead of using searchForNeighbors.
228 
229  // For each scale, compute the Gaussian "filter response" at the current point
230  float filter_response = 0.0f;
231  float previous_filter_response;
232  for (size_t i_scale = 0; i_scale < scales.size (); ++i_scale)
233  {
234  float sigma_sqr = powf (scales[i_scale], 2.0f);
235 
236  float numerator = 0.0f;
237  float denominator = 0.0f;
238  for (size_t i_neighbor = 0; i_neighbor < nn_indices.size (); ++i_neighbor)
239  {
240  const float &value = getFieldValue_ (input.points[nn_indices[i_neighbor]]);
241  const float &dist_sqr = nn_dist[i_neighbor];
242  if (dist_sqr <= 9*sigma_sqr)
243  {
244  float w = expf (-0.5f * dist_sqr / sigma_sqr);
245  numerator += value * w;
246  denominator += w;
247  }
248  else break; // i.e. if dist > 3 standard deviations, then terminate early
249  }
250  previous_filter_response = filter_response;
251  filter_response = numerator / denominator;
252 
253  // Compute the difference between adjacent scales
254  if (i_scale > 0)
255  diff_of_gauss (i_point, i_scale - 1) = filter_response - previous_filter_response;
256  }
257  }
258 }
259 
260 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
261 template <typename PointInT, typename PointOutT> void
263  const PointCloudIn &input, KdTree &tree, const Eigen::MatrixXf &diff_of_gauss,
264  std::vector<int> &extrema_indices, std::vector<int> &extrema_scales)
265 {
266  const int k = 25;
267  std::vector<int> nn_indices (k);
268  std::vector<float> nn_dist (k);
269 
270  const int nr_scales = static_cast<int> (diff_of_gauss.cols ());
271  std::vector<float> min_val (nr_scales), max_val (nr_scales);
272 
273  for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
274  {
275  // Define the local neighborhood around the current point
276  const size_t nr_nn = tree.nearestKSearch (i_point, k, nn_indices, nn_dist); //*
277  // * note: the neighborhood for finding local extrema is best defined as a small fixed-k neighborhood, regardless of
278  // the configurable search method specified by the user, so we directly employ tree.nearestKSearch here instead
279  // of using searchForNeighbors
280 
281  // At each scale, find the extreme values of the DoG within the current neighborhood
282  for (int i_scale = 0; i_scale < nr_scales; ++i_scale)
283  {
284  min_val[i_scale] = std::numeric_limits<float>::max ();
285  max_val[i_scale] = -std::numeric_limits<float>::max ();
286 
287  for (size_t i_neighbor = 0; i_neighbor < nr_nn; ++i_neighbor)
288  {
289  const float &d = diff_of_gauss (nn_indices[i_neighbor], i_scale);
290 
291  min_val[i_scale] = (std::min) (min_val[i_scale], d);
292  max_val[i_scale] = (std::max) (max_val[i_scale], d);
293  }
294  }
295 
296  // If the current point is an extreme value with high enough contrast, add it as a keypoint
297  for (int i_scale = 1; i_scale < nr_scales - 1; ++i_scale)
298  {
299  const float &val = diff_of_gauss (i_point, i_scale);
300 
301  // Does the point have sufficient contrast?
302  if (fabs (val) >= min_contrast_)
303  {
304  // Is it a local minimum?
305  if ((val == min_val[i_scale]) &&
306  (val < min_val[i_scale - 1]) &&
307  (val < min_val[i_scale + 1]))
308  {
309  extrema_indices.push_back (i_point);
310  extrema_scales.push_back (i_scale);
311  }
312  // Is it a local maximum?
313  else if ((val == max_val[i_scale]) &&
314  (val > max_val[i_scale - 1]) &&
315  (val > max_val[i_scale + 1]))
316  {
317  extrema_indices.push_back (i_point);
318  extrema_scales.push_back (i_scale);
319  }
320  }
321  }
322  }
323 }
324 
325 #define PCL_INSTANTIATE_SIFTKeypoint(T,U) template class PCL_EXPORTS pcl::SIFTKeypoint<T,U>;
326 
327 #endif // #ifndef PCL_SIFT_KEYPOINT_IMPL_H_
328 
SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset ...
Definition: sift_keypoint.h:94
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:62
void filter(PointCloud &output)
Calls the filtering method and returns the filtered dataset in output.
Definition: filter.h:132
VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data...
Definition: voxel_grid.h:178
void setScales(float min_scale, int nr_octaves, int nr_scales_per_octave)
Specify the range of scales over which to search for keypoints.
void setMinimumContrast(float min_contrast)
Provide a threshold to limit detection of keypoints without sufficient contrast.
PointCloud represents the base class in PCL for storing collections of 3D points. ...
void detectKeypoints(PointCloudOut &output)
Detect the SIFT keypoints for a set of points given in setInputCloud () using the spatial locator in ...
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:66
KdTree represents the base spatial locator class for kd-tree implementations.
Definition: kdtree.h:56
void setLeafSize(const Eigen::Vector4f &leaf_size)
Set the voxel grid leaf size.
Definition: voxel_grid.h:223