Add changes for 4d71470631

modules/mapping/point_cloud_sampling/point_cloud_sampling.html

@@ -154,7 +154,7 @@ points.</p>
 <h3>API<a class="headerlink" href="#api" title="Permalink to this headline"></a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling">
-<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">voxel_point_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._generic_alias.ScalarType</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#voxel_point_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">voxel_point_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._array_like._ScalarType_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#voxel_point_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling" title="Permalink to this definition"></a></dt>
 <dd><p>Voxel Point Sampling function.
 This function sample N-dimensional points with voxel grid.
 Points in a same voxel grid will be merged by mean operation for sampling.</p>
@@ -190,7 +190,7 @@ you want to obtain a specified number of points from point cloud.</p>
 <h3>API<a class="headerlink" href="#id2" title="Permalink to this headline"></a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling">
-<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">farthest_point_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._generic_alias.ScalarType</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#farthest_point_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">farthest_point_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._array_like._ScalarType_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#farthest_point_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling" title="Permalink to this definition"></a></dt>
 <dd><p>Farthest point sampling function
 This function sample N-dimensional points with the farthest point policy.</p>
 <dl class="field-list simple">
@@ -227,7 +227,7 @@ this is suitable for real-time processing because of its fast computation time.<
 <h3>API<a class="headerlink" href="#id3" title="Permalink to this headline"></a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling">
-<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">poisson_disk_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._generic_alias.ScalarType</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_distance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MAX_ITER</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#poisson_disk_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">poisson_disk_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._array_like._ScalarType_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_distance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MAX_ITER</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#poisson_disk_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling" title="Permalink to this definition"></a></dt>
 <dd><p>Poisson disk sampling function
 This function sample N-dimensional points randomly until the number of
 points keeping minimum distance between selected points.</p>