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1.12.0
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<div class="local-toc"><ul>
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<li><a class="reference internal" href="#">Getting Started / Basic Structures</a></li>
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<li><a class="reference internal" href="#compiling-your-first-code-example">Compiling your first code example</a></li>
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<li><a href="index.html">Docs</a> »</li>
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<li>Getting Started / Basic Structures</li>
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<div class="section" id="getting-started-basic-structures">
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<span id="basic-structures"></span><h1>Getting Started / Basic Structures</h1>
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<p>The basic data type in PCL 1.x is a <span>PointCloud</span>. A
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PointCloud is a C++ class which contains the following data fields:</p>
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<blockquote>
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<div><ul>
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<li><p class="first"><span>width</span> (int)</p>
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<p>Specifies the width of the point cloud dataset in the number of points. <em>width</em> has two meanings:</p>
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<blockquote>
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<div><ul class="simple">
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<li>it can specify the total number of points in the cloud (equal with the number of elements in <strong>points</strong> – see below) for unorganized datasets;</li>
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<li>it can specify the width (total number of points in a row) of an organized point cloud dataset.</li>
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</ul>
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</div></blockquote>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p class="last">An <strong>organized point cloud</strong> dataset is the name given to point clouds
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that resemble an organized image (or matrix) like structure, where the
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data is split into rows and columns. Examples of such point clouds
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include data coming from stereo cameras or Time Of Flight cameras. The
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advantages of an organized dataset is that by knowing the relationship
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between adjacent points (e.g. pixels), nearest neighbor operations are
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much more efficient, thus speeding up the computation and lowering the
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costs of certain algorithms in PCL.</p>
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</div>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p class="last">An <strong>projectable point cloud</strong> dataset is the name given to point clouds
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that have a correlation according to a pinhole camera model between the (u,v) index
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of a point in the organized point cloud and the actual 3D values. This correlation can be
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expressed in it’s easiest form as: u = f*x/z and v = f*y/z</p>
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</div>
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<p>Examples:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cloud</span><span class="o">.</span><span class="n">width</span> <span class="o">=</span> <span class="mi">640</span><span class="p">;</span> <span class="o">//</span> <span class="n">there</span> <span class="n">are</span> <span class="mi">640</span> <span class="n">points</span> <span class="n">per</span> <span class="n">line</span>
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</pre></div>
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</div>
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</li>
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<li><p class="first"><span>height</span> (int)</p>
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<p>Specifies the height of the point cloud dataset in the number of points. <em>height</em> has two meanings:</p>
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<blockquote>
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<div><ul class="simple">
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<li>it can specify the height (total number of rows) of an organized point cloud dataset;</li>
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<li>it is set to <strong>1</strong> for unorganized datasets (<em>thus used to check whether a dataset is organized or not</em>).</li>
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</ul>
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<p>Example:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cloud</span><span class="o">.</span><span class="n">width</span> <span class="o">=</span> <span class="mi">640</span><span class="p">;</span> <span class="o">//</span> <span class="n">Image</span><span class="o">-</span><span class="n">like</span> <span class="n">organized</span> <span class="n">structure</span><span class="p">,</span> <span class="k">with</span> <span class="mi">480</span> <span class="n">rows</span> <span class="ow">and</span> <span class="mi">640</span> <span class="n">columns</span><span class="p">,</span>
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<span class="n">cloud</span><span class="o">.</span><span class="n">height</span> <span class="o">=</span> <span class="mi">480</span><span class="p">;</span> <span class="o">//</span> <span class="n">thus</span> <span class="mi">640</span><span class="o">*</span><span class="mi">480</span><span class="o">=</span><span class="mi">307200</span> <span class="n">points</span> <span class="n">total</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">dataset</span>
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</pre></div>
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</div>
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<p>Example:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cloud</span><span class="o">.</span><span class="n">width</span> <span class="o">=</span> <span class="mi">307200</span><span class="p">;</span>
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<span class="n">cloud</span><span class="o">.</span><span class="n">height</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span> <span class="o">//</span> <span class="n">unorganized</span> <span class="n">point</span> <span class="n">cloud</span> <span class="n">dataset</span> <span class="k">with</span> <span class="mi">307200</span> <span class="n">points</span>
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</pre></div>
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</div>
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</div></blockquote>
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</li>
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<li><p class="first"><span>points</span> (std::vector<PointT>)</p>
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<p>Contains the data array where all the points of type <strong>PointT</strong> are stored. For example, for a cloud containing XYZ data, <strong>points</strong> contains a vector of <em>pcl::PointXYZ</em> elements:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pcl</span><span class="p">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="p">::</span><span class="n">PointXYZ</span><span class="o">></span> <span class="n">cloud</span><span class="p">;</span>
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<span class="n">std</span><span class="p">::</span><span class="n">vector</span><span class="o"><</span><span class="n">pcl</span><span class="p">::</span><span class="n">PointXYZ</span><span class="o">></span> <span class="n">data</span> <span class="o">=</span> <span class="n">cloud</span><span class="o">.</span><span class="n">points</span><span class="p">;</span>
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</pre></div>
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</div>
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</li>
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<li><p class="first"><span>is_dense</span> (bool)</p>
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<p>Specifies if all the data in <strong>points</strong> is finite (true), or whether the XYZ values of certain points might contain Inf/NaN values (false).</p>
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</li>
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<li><p class="first"><span>sensor_origin_</span> (Eigen::Vector4f)</p>
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<p>Specifies the sensor acquisition pose (origin/translation). This member is usually optional, and not used by the majority of the algorithms in PCL.</p>
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</li>
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<li><p class="first"><span>sensor_orientation_</span> (Eigen::Quaternionf)</p>
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<p>Specifies the sensor acquisition pose (orientation). This member is usually optional, and not used by the majority of the algorithms in PCL.</p>
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</li>
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</ul>
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</div></blockquote>
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<p>To simplify development, the <span>PointCloud</span> class contains
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a number of helper member functions. For example, users don’t have to check if
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<strong>height</strong> equals 1 or not in their code in order to see if a dataset is
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organized or not, but instead use <span>PointCloud</span>:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>if (!cloud.isOrganized ())
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...
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</pre></div>
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</div>
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<p>The <strong>PointT</strong> type is the primary point data type and describes what each
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individual element of <span>points</span> holds. PCL comes
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with a large variety of different point types, most explained in the
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<a class="reference internal" href="adding_custom_ptype.html#adding-custom-ptype"><span class="std std-ref">Adding your own custom PointT type</span></a> tutorial.</p>
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</div>
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<div class="section" id="compiling-your-first-code-example">
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<h1>Compiling your first code example</h1>
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<p>Until we find the right minimal code example, please take a look at the
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<a class="reference internal" href="using_pcl_pcl_config.html#using-pcl-pcl-config"><span class="std std-ref">Using PCL in your own project</span></a> and <a class="reference internal" href="writing_new_classes.html#writing-new-classes"><span class="std std-ref">Writing a new PCL class</span></a> tutorials to see how
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to compile and write code for or using PCL.</p>
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