341 lines
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HTML
341 lines
9.7 KiB
HTML
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<!DOCTYPE html>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Benchmarking 3D — Point Cloud Library 1.12.0 documentation</title>
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<a href="index.html" class="icon icon-home"> Point Cloud Library
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<div class="version">
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1.12.0
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<!-- Local TOC -->
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<div class="local-toc"><ul>
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<li><a class="reference internal" href="#">Benchmarking 3D</a></li>
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<li><a class="reference internal" href="#benchmarking-object-recognition">Benchmarking Object Recognition</a><ul>
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<li><a class="reference internal" href="#training">1. Training</a></li>
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<li><a class="reference internal" href="#keypoints">2. Keypoints</a></li>
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<li><a class="reference internal" href="#descriptors">3. Descriptors</a></li>
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<li><a class="reference internal" href="#classification">4. Classification</a></li>
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<li><a class="reference internal" href="#evaluation">5. Evaluation</a><ul>
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<li><a class="reference internal" href="#metrics">5.1 Metrics</a><ul>
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<li><a class="reference internal" href="#object-recognition-api">Object Recognition API</a></li>
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</ul>
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</li>
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</ul>
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</li>
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<li><a class="reference internal" href="#id1">1. Training</a></li>
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<li><a class="reference internal" href="#id2">2. Keypoints</a></li>
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<li><a class="reference internal" href="#id3">3. Descriptors</a></li>
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<li><a class="reference internal" href="#id4">4. Classification</a></li>
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<li><a class="reference internal" href="#id5">5. Evaluation</a></li>
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</ul>
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</li>
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</ul>
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<a href="index.html">Point Cloud Library</a>
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<li><a href="index.html">Docs</a> »</li>
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<li>Benchmarking 3D</li>
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<hr/>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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<div itemprop="articleBody">
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<div class="section" id="benchmarking-3d">
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<span id="benchmarking"></span><h1>Benchmarking 3D</h1>
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<p>This document introduces benchmarking concepts for 3D algorithms. By
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<em>benchmarking</em> here we refer to the possibility of testing different
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computational pipelines in an <strong>easy manner</strong>. The goal is to test their
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reproductibility with respect to a particular problem of general interest.</p>
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</div>
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<div class="section" id="benchmarking-object-recognition">
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<h1>Benchmarking Object Recognition</h1>
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<p>For the general problem of Object Recognition (identification, categorization,
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detection, etc – all fall in the same category here), we identify the
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following steps:</p>
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<div class="section" id="training">
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<h2>1. Training</h2>
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<p>Users should be able to acquire training data from different inputs, including
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but not limited to:</p>
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<blockquote>
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<div><ul class="simple">
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<li>full triangle meshes (CAD models);</li>
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<li>360-degree full point cloud models;</li>
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<li>partial point cloud views:<ul>
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<li>in clutter;</li>
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<li>cleanly segmented.</li>
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</ul>
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</li>
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</ul>
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</div></blockquote>
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</div>
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<div class="section" id="keypoints">
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<h2>2. Keypoints</h2>
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<p>Computing higher level representation from the object’s appearance (texture + depth) should be done:</p>
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<blockquote>
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<div><ul class="simple">
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<li><strong>densely</strong> - at every point/vertex in the input data;</li>
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<li>at certain <strong>interest points</strong> (i.e., keypoints).</li>
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</ul>
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</div></blockquote>
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<p>The detected keypoint might also contain some meta-information required by some descriptors, like scale or orientation.</p>
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</div>
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<div class="section" id="descriptors">
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<h2>3. Descriptors</h2>
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<p>A higher level representation as mentioned before will be herein represented by a <strong>feature descriptor</strong>. Feature descriptors can be:</p>
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<blockquote>
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<div><ul class="simple">
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<li>2D (two-dimensional) – here we refer to those descriptors estimated solely from RGB texture data;</li>
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<li>3D (three-dimensional) – here we refer to those descriptors estimated solely from XYZ/depth data;</li>
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<li>a combination of the above.</li>
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</ul>
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</div></blockquote>
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<p>In addition, feature descriptors can be:</p>
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<blockquote>
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<div><ul class="simple">
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<li><strong>local</strong> - estimated only at a set of discrete keypoints, using the information from neighboring pixels/points;</li>
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<li><strong>global</strong>, or meta-local - estimated on entire objects or the entire input dataset.</li>
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</ul>
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</div></blockquote>
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</div>
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<div class="section" id="classification">
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<h2>4. Classification</h2>
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<p>The distribution of features should be classifiable into distinct, separable
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classes. For local features, we identify two sets of techniques:</p>
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<blockquote>
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<div><ul class="simple">
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<li><strong>bag of words</strong>;</li>
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<li><strong>voting</strong>;</li>
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<li><strong>supervised voting</strong> (regression from the description to the relative 3D location, e.g. Hough forest).</li>
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</ul>
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</div></blockquote>
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<p>For global features, any general purpose classification technique should work (e.g., SVMs, nearest neighbors, etc).</p>
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<p>In addition to classification, a substep of it could be considered
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<strong>Registration</strong>. Here we refine the classification results using iterative
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closest point techniques for example.</p>
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</div>
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<div class="section" id="evaluation">
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<h2>5. Evaluation</h2>
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<p>This pipeline should be able to evaluate the algorithm’s performance at
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different tasks. Here are some requested tasks to support:</p>
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<blockquote>
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<div><ul class="simple">
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<li>object id and pose</li>
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<li>object id and segmentation</li>
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<li>object id and bounding box</li>
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<li>category and segmentation</li>
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<li>category and bounding box</li>
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</ul>
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</div></blockquote>
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<div class="section" id="metrics">
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<h3>5.1 Metrics</h3>
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<p>This pipeline should provide different metrics, since algorithms excel in
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different areas. Here are some requested metrics:</p>
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<blockquote>
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<div><ul class="simple">
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<li>precision-recall</li>
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<li>time</li>
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<li>average rank of correct id</li>
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<li>area under curve of cumulative histogram of rank of correct id</li>
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</ul>
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</div></blockquote>
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<div class="section" id="object-recognition-api">
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<h4>Object Recognition API</h4>
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<p>Here we describe a proposed set of classes that could be easily extended and
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used for the purpose of benchmarking object recognition tasks.</p>
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</div>
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</div>
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</div>
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<div class="section" id="id1">
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<h2>1. Training</h2>
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</div>
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<div class="section" id="id2">
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<h2>2. Keypoints</h2>
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</div>
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<div class="section" id="id3">
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<h2>3. Descriptors</h2>
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</div>
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<div class="section" id="id4">
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<h2>4. Classification</h2>
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</div>
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<div class="section" id="id5">
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<h2>5. Evaluation</h2>
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<p>The evaluation output needs to be one of the following:</p>
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<blockquote>
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<div><ul class="simple">
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<li>object id</li>
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<li>object pose</li>
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<li>object category</li>
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<li>object bounding box</li>
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<li>object mask</li>
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</ul>
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</div></blockquote>
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</div>
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</div>
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© Copyright
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