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<!doctype html>
<html lang="en">
<head>
<link href="captionss/captionss.css" rel="stylesheet" type="text/css">
<meta charset="utf-8">
<title>Supervoxels - Temporal Voxel Manifolds and Applications</title>
<meta name="author" content="Jérémie Papon">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<link rel="stylesheet" href="reveal/css/reveal.min.css">
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<link rel="stylesheet" href="reveal/lib/css/zenburn.css">
<script>
document.write('<link rel="stylesheet" href="reveal/css/print/'+(window.location.search.match(/print-pdf/gi) ? 'pdf' : 'paper')+'.css" type="text/css" media="print">');
</script>
<!--[if lt IE 9]>
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.edgeLoad-EDGE-3489513 { visibility:hidden; }
</style>
<!--Load HTVF animation -->
<script>
AdobeEdge.loadComposition('HTVF_anim2', 'EDGE-3489513', {
scaleToFit: "none",
centerStage: "both",
minW: "0",
maxW: "undefined",
width: "1024px",
height: "625px"
}, {dom: [ ]}, {dom: [ ]});
</script>
<!--Adobe Edge Runtime End-->
</head>
<body>
<div class="reveal">
<resetfootnotecounter></resetfootnotecounter>
<resetcitationcounter></resetcitationcounter>
<div class="slides">
<section>
<p style="text-align: center"><br><br><br>Press F11 to view this in full screen.</p>
<p style="text-align: center">Press Left/Right to advance through the presentation.</p>
<p style="text-align: center">Make sure to click on the play button for Point Clouds!</p>
<p style="text-align: center">Don't miss the vertical slides - you'll see up/down arrows on the bottom right!</p>
<p style="text-align: center">You can press the "esc" key to go to a slide overview.</p>
<br>
<p style="text-align: center"> For more information on this work, visit <a href="http://www.jeremiepapon.com/">http://www.jeremiepapon.com/ </a></p>
</section>
<section data-background="data/Buildings_Cropped_Merge.png">
<h1 style="text-shadow:4px 4px #000000">Supervoxels</h1>
<h3 style="text-shadow:3px 3px #000000">Temporal Voxel Manifolds and their Applications</h3>
<footer style="font-size: 36px">
<div> <a style="color: #00FF00;text-shadow:4px 4px #000000" href="http://www.jeremiepapon.com/"><strong>Jérémie Papon</strong></a> </div>
<div style="text-shadow:3px 3px #000000">
Georg-August-Universität Göttingen <br>
Bernstein Center for Computational Neuroscience <br>
Institut für Informatik <br>
5 March 2015
</div>
</footer>
</section>
<!-- MOTIVATION -->
<section>
<h2>How do we learn to perceive objects?</h2>
<blockquote style="font-size: 1.2em; line-height: 125%; background:">“Infants appear to perceive objects by analyzing <span class="fragment highlight-green">three-dimensional surface arrangements and motions </span>... [they] divide perceptual arrays into <span class="fragment highlight-red">units that move as connected wholes</span>, <span class="fragment highlight-blue">that move separately from one another</span>, that tend to <span class="fragment highlight-green">maintain their size and shape over motion</span>, and that tend to <span class="fragment highlight-red">act upon each other only on contact.</span>” *</blockquote>
<br>
<div class="w50" style="float: right">
<img src="data/child_colors.jpg" width="70%">
</div>
<div class="w50">
<video src="data/movies/cutting.mp4" width="75%" controls loop class="slideautostart"></video>
</div>
<p> How can we track these units without a-priori object knowledge? </p>
<div class='footer' >
* Spelke, Elizabeth S. "Principles of object perception." Cognitive science 14, no. 1 (1990): 29-56.
</div>
</section>
<section>
<h2>Temporal Connections without Objects</h2>
<p>How can we create partitions when we don't know what an object is before-hand?</p>
<video src="data/movies/cutting.mp4" width="70%" controls loop class="slideautostart"></video>
<p>We have no difficulty tracking the pieces of objects when they split.</p>
<ul>
<li>This implies maintenance of both low-level and object-level spatio-temporal tracking.</li>
</ul>
</section>
<!-- Existing Stuff -->
<section>
<section>
<h2>Parsing Video Streams -<br> Existing Methodologies</h2>
<p><strong>Video Object Segmentation</strong>
e.g.Abramov et al.<cite></cite>
Grundmann et al.<cite></cite></p>
<p>This parses a video into spatio-temporal volumes - “objects”</p>
<p>Core assumption means that “objects” must form <span class="fragment highlight-green">continuous spatio-temporal</span> volumes!</p>
<video src="data/movies/grundmann/occlusion_grund03_append.mp4" height="60%" controls loop class="slideautostart"></video>
<p class="rcred"><a href="http://www.videosegmentation.com/">Processed on VideoSegmentation.com</a></p>
<br>
<div class='citation'>
<footl>
<footi>Abramov et al., <a href="http://dx.doi.org/10.1109/TCSVT.2012.2199389">Real-Time Segmentation of Stereo Videos on a Portable System With a Mobile GPU,</a> <em>IEEE Transactions on Circuits and Systems for Video Technology </em>2012.</footi>
<footi>Grundmann et al., <a href="http://www.cc.gatech.edu/cpl/projects/videosegmentation/cvpr2010_videosegmentation.pdf">Efficient Hierarchical Graph Based Video Segmentation,</a><em>Computer Vision and Pattern Recognition (CVPR)</em> 2010.</footi>
</footl>
</div>
</section>
<section>
<h2>Parsing Video Streams -<br> Existing Methodologies</h2>
<video src="data/movies/grundmann/cutting_grund03.mp4" height="70%" controls loop class="slideautostart"></video>
<p class="rcred"><a href="http://www.videosegmentation.com/">Processed on VideoSegmentation.com</a></p>
<div>Complete failure if this assumption is violated. </div>
</section>
</section>
<section>
<h2>Parsing Video Streams -<br> Existing Methodologies</h2>
<p><strong>Semantic Event Chains</strong><cite></cite> - Represents by analyzing creation & deletion of edges in segment adjacency graph.</p>
<p>Analysis of temporal evolution of graph structure yields semantics</p>
<video src="data/movies/eren/maniac_breakfast.mp4" height="60%" controls loop class="slideautostart"></video>
<p class="rcred"><a href="https://www.youtube.com/watch?v=Gt5TVEcSTTE">Maniac Dataset: Breakfast</a></p>
<div>This requires <span class="fragment highlight-green">a-priori knowledge</span> of objects!</div>
<br>
<div class='citation'>
<footl>
<footi>Aksoy, Eren Erdal, et al. <a href="http://www.dpi.physik.uni-goettingen.de/~eaksoye/papers/IJRR_2011.pdf">Learning the semantics of object–action relations by observation.</a> <em>The International Journal of Robotics Research</em> (2011).</footi>
</footl>
</div>
</section>
<!--
<section style="line-height: 135%">
<h2>Transitioning to 3D</h2>
<p>To Summarize most vexing issues </p>
<ul>
<li class="fragment">Segment into spatio-temporal volumes - <span class="fragment current-visible">cannot handle occlusions</span></li>
<li class="fragment">Divide the scene into objects <em><span class="fragment highlight-green">before</span></em> observations.</li>
<ul style="list-style-type: none"><li class="fragment">Cannot learn “object-ness” from observations</li></ul>
<li class="fragment"> Only use color - 3D geometry is not considered </li>
</ul>
<p class="fragment">To overcome some of these, we use RGB-D sensors to capture Point Clouds</p>
<div class="ctr w70">
<figure class="embed hide-smooth dark" >
<img src="data/openni_cams.jpg">
<figcaption style="font-size:0.75em">
Some OpenNI Sensors which capture RGB+D data.
</figcaption>
</figure>
</div>
<p class="rcred"><a href="http://www.pointclouds.org/">Point Cloud Library (PCL)</a></p>
</section>
-->
<section>
<h2>Overview of Methodology</h2>
<div>
<img src="data/Overall_Cropped.svg" width="100%">
</div>
<div style="position:absolute;left:785px;top:137px;">
<video src="data/movies/cutting_noocc_result_png.mp4" height="50%" loop class="slideautostart"></video>
</div>
</section>
<section>
<h2>A Point Cloud</h2>
<div align="center">
<iframe src="http://pointclouds.org/assets/viewer/pcl_viewer.html?load=http://jpapon.github.io/data/pointclouds/cutting_demo_cloud.pcd&psize=3" width="1100" height="650" marginwidth="0" marginheight="0" frameborder="no" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="" style="max-width: 100%;">
</iframe>
</div>
<p>Advantages of 3D</p>
<ul>
<li> Avoids size/shape ambiguities of perspective transformation. </li>
<li> Can reason about occlusions at a low level. </li>
<li> Can use size and shape as a feature. </li>
</ul>
</section>
<!--
<section>
<h2>Octree Voxelization</h2>
<div class="w45" style="float: right">
<img src="data/bunnywork.png">
<p class="rcred"><a href="http://www.pointclouds.org/">Point Cloud Library (PCL)</a></p>
</div>
<p>Insert pointcloud into a grid of cubic voxels.</p>
<p>Represent all points in one cell by its centroid.</p>
<p>$$\vec{ \overline{p}} = \frac{1}{N_i}\sum_i \vec p_i$$</p>
<p>The edge length $L_\text{voxel}$ of voxels defines scale of observation and determines octree minimum bin size.</p>
</section>
<section>
<h2>A Voxelized Point Cloud</h2>
<iframe src="http://pointclouds.org/assets/viewer/pcl_viewer.html?load=http://134.76.92.76/data/cutting_demo_cloud_voxel_trans.pcd&psize=2" width="1100" height="750" marginwidth="0" marginheight="0" frameborder="no" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="">
</iframe>
</section>
-->
<section>
<h2>Building an Adjacency Graph</h2>
<ul>
<li class="fragment">Special octree type developed which maintains adjacency information of voxels</li>
<li class="fragment">This gives us back pixel-like (grid) relations, while keeping real 3D adjacency</li>
<li class="fragment">Region growing and connectivity graph become very efficient</li>
</ul>
<div class="ctr w80">
<figure class="embed hide-smooth dark" >
<img src="data/AdjacencyOctree.svg">
<figcaption style="font-size:1.0em">
Octree Adjacency Structure - Leaves now link to their spatial neighbors.
</figcaption>
</figure>
</div>
</section>
<section>
<h2>Voxel Cloud Connectivity Segmentation</h2>
<ul>
<li class="fragment">VCCS <cite></cite> is a region-growing oversegmentation technique that uses local geometry to respect object boundaries</li>
<li class="fragment">Constrained to flow across voxel connections</li>
<li class="fragment">Use color, normals, and a spatial smoothness constraint </li>
</ul>
<div style="width:50%; float:left">
<figure class="embed hide-smooth dark" >
<img src="data/test55.png"">
<figcaption style="font-size:1.0em">
Test Scene
</figcaption>
</figure>
</div>
<div style="width:50%; float:right">
<figure class="embed hide-smooth dark" >
<img src="data/supervoxel-growth.gif">
<figcaption style="font-size:1.0em">
Iterative Expansion of Supervoxels using VCCS
</figcaption>
</figure>
<div>
<p class="rcred"><a href="http://users.acin.tuwien.ac.at/arichtsfeld/?site=4">OSD Dataset</a> <a href="http://www.pointclouds.org/blog/tocs/alexandrov/index.php">Sergey Alexandrov</a></p>
<br>
<div class='citation'>
<footl>
<footi>Papon et al., <a href="http://www.jeremiepapon.com/cvpr-2013-supervoxels/">Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds,</a> Computer Vision and Pattern Recognition (CVPR) 2013.</footi>
</footl>
</div>
</section>
<section>
<section>
<h2>Examples of Supervoxels</h2>
<p> Example of Supervoxels with different seed sizes - from NYU Dataset <cite></cite> </p>
<div class="ctr w100">
<img src="data/IncreasingSeedSizePlain.svg" width="100%" >
<p class="rcred"><a href="http://www.jeremiepapon.com/cvpr-2013-supervoxels/">Papon et al. CVPR 2013</a></p>
</div>
<div class="ctr" style="width:75%">
<figure class="embed-top hide-smooth dark" >
<img src="data/VCCS_Performance.svg">
<figcaption style="font-size:1.0em">
Performance of VCCS Compared to state of the art methods
</figcaption>
</figure>
</div>
<br>
<div class="citation">
<footl>
<footi>Silberman et al., <a href="http://cs.nyu.edu/~silberman/projects/indoor_scene_seg_sup.html">Indoor Segmentation and Support Inference from RGBD Images,</a> European Conference on Computer Vision (ECCV) 2012.</footi>
</footl>
</div>
</section>
<section>
<h2>Quantitative Comparison to SLIC</h2>
<div>
<figure class="embed-top hide-smooth dark" >
<img src="data/IncreasingSeedSizePlain.svg">
<figcaption style="font-size:1.0em">
VCCS Supervoxels for increasing seed size.
</figcaption>
</figure>
<p class="rcred"><a href="http://www.jeremiepapon.com/cvpr-2013-supervoxels/">Papon et al. CVPR 2013</a></p>
</div>
<div>
<figure class="embed-top hide-smooth dark" >
<img src="data/ComparisonToSLIC.svg">
<figcaption style="font-size:1.0em">
SLIC Superpixels
</figcaption>
</figure>
</div>
<br>
<div class="citation" >
<footl>
<footi>Achanta et al., <a href="http://ivrg.epfl.ch/research/superpixels">SLIC Superpixels Compared to State-of-the-art Superpixel Methods, </a> IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.</footi>
</footl>
</div>
</section>
<section>
<h2> Speed and Performance vs State of the Art </h2>
<div class="ctr" style="width:80%">
<figure class="embed-top hide-smooth dark" >
<img src="data/VCCS_Performance.svg">
<figcaption style="font-size:1.0em">
Performance of VCCS Compared to state of the art methods
</figcaption>
</figure>
</div>
<div class="ctr" style="width:80%">
<figure class="embed-top hide-smooth dark" >
<img src="data/VCCS_Speed.svg">
<figcaption style="font-size:1.0em">
Speed of VCCS Compared to state of the art methods
</figcaption>
</figure>
</div>
</section>
</section>
<section>
<h2>Supervoxels in a Point Cloud</h2>
<iframe src="http://pointclouds.org/assets/viewer/pcl_viewer.html?load=http://jpapon.github.io/data/pointclouds/cutting_demo_cloud_supervoxels.pcd&psize=3" width="1100" height="750" marginwidth="0" marginheight="0" frameborder="no" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="">
</iframe>
</section>
<section>
<section>
<h2>Local Convexity Segmentation (LCCP)<cite></cite></h2>
<p>Use a local convexity criterion on adjacency graph edges to split graph.</p> <br>
<div class="ctr w100">
<figure class="embed reveal-smooth dark" >
<img src="data/algorithmic_flow.svg">
<figcaption style="font-size:1.0em">
Flow of segmentation: voxels to supervoxels to local convex patches.
</figcaption>
</figure>
</div>
<br>
<div class='citation'>
<footl>
<footi>Stein, S.; Schoeler, M.; <strong>Papon, J.</strong>; Wörgötter, F., <a href="http://www.jeremiepapon.com/cvpr-2014-segmentation/">Object Partitioning using Local Convexity,</a> Computer Vision and Pattern Recognition (CVPR) 2014, June 2014.</footi>
</footl>
</div>
</section>
<section>
<div class="ctr w100">
<figure class="embed hide-smooth dark" >
<img src="data/NYU_LCCP_Examples.png">
<figcaption style="font-size:1.0em">
LCCP Example Results
</figcaption>
</figure>
</div>
</section>
<section>
<div class="ctr w100">
<figure class="embed hide-smooth dark" >
<img src="data/lccp_result_images1.svg">
<figcaption style="font-size:1.0em">
LCCP Example Results
</figcaption>
</figure>
</div>
</section>
<section>
<div class="ctr w100">
<figure class="embed hide-smooth dark" >
<img src="data/lccp_result_images2.svg">
<figcaption style="font-size:1.0em">
LCCP Example Results
</figcaption>
</figure>
</div>
</section>
<section>
<div class="ctr w100">
<figure class="embed-top hide-smooth dark" >
<img src="data/OSD_results.png">
<figcaption style="font-size:1.0em">
LCCP Comparison on OSD Dataset
</figcaption>
</figure>
<br>
<figure class="embed-top hide-smooth dark" >
<img src="data/NYU_results.png">
<figcaption style="font-size:1.0em">
LCCP Comparison on NYU Dataset
</figcaption>
</figure>
</div>
</section>
</section>
<section>
<h2>LCCP Segments in a Point Cloud</h2>
<iframe src="http://pointclouds.org/assets/viewer/pcl_viewer.html?load=http://jpapon.github.io/data/pointclouds/cutting_demo_cloud_lccp.pcd&psize=3" width="1100" height="750" marginwidth="0" marginheight="0" frameborder="no" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="">
</iframe>
</section>
<section>
<h2>CPC- Constrained Planar Cuts</h2>
<p>LCCP has a very strict cutting criterion, now we will relax it.</p>
<div class="ctr w70">
<figure class="embed reveal-smooth dark" >
<img src="data/LCCP_Fail.svg">
<figcaption style="font-size:1.0em">
LCCP Fails when concavities don't completely isolate parts.
</figcaption>
</figure>
</div>
<br>
<div class='citation'>
<footl>
<footi>Schoeler, M.; <strong>Papon, J.</strong>; Wörgötter, F., Constrained Planar Cuts - Object Partitioning for Point Clouds,</a> Computer Vision and Pattern Recognition (CVPR) 2015, June 2015.</footi>
</footl>
</div>
</section>
<section>
<h2>CPC- Constrained Planar Cuts</h2>
<div class="ctr w100">
<div class="ctr w70">
<figure class="embed reveal-smooth dark" >
<img src="data/Chair.svg">
<figcaption style="font-size:1.0em">
Find planar cuts which score highest - most concavities in support region.
</figcaption>
</figure>
</div>
<ul>
<li class="fragment">Extract concavity adjacency graph using Supervoxels, LCCP algorithm</li>
<li class="fragment">Use weighted RANSAC planar cuts to find highest scoring cuts.</li>
<li class="fragment">Segmentation proceeds hierarchicaly, terminating once score below threshold. </li>
</ul>
<div class="ctr w100 fragment">
<figure class="embed reveal-smooth dark" >
<img src="data/Steps.svg">
<figcaption style="font-size:1.0em">
Cuts proceed recursively until score threshold reached.
</figcaption>
</figure>
</div>
</div>
</section>
<section>
<section>
<h2>Results on Princeton Segmentation Benchmark</h2>
<div class="ctr w90">
<figure class="embed reveal-smooth dark" >
<img src="data/Princeton_Qualitative.svg">
<figcaption style="font-size:1.0em">
Typical Results on Benchmark Dataset
</figcaption>
</figure>
</div>
<br>
<div class='citation'>
<footl>
<footi>X. Chen et al. A benchmark for 3d mesh segmentation.,</a>in Transactions on Graphics, 2009.</footi>
</footl>
</div>
</section>
<section>
<h2>Directed Cuts</h2>
<div class="ctr w90">
<figure class="embed reveal-smooth dark" >
<img src="data/Directed_vs_undirected_weights.svg">
<figcaption style="font-size:1.0em">
Cuts are weighted by relation to concave connection being cut
</figcaption>
</figure>
</div>
</section>
<section>
<h2>Quantitiative Results</h2>
<div class="ctr w90">
<figure class="embed reveal-smooth dark" >
<img src="data/Princeton_Quantitative.svg">
<figcaption style="font-size:1.0em">
Quantitiative Measures on Princeton Dataset
</figcaption>
</figure>
</div>
</section>
</section>
<section>
<h2> Can segment huge full 3D scenes efficiently. </h2>
<video src="data/movies/city_loop_lores.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<h2>Overview of Methodology</h2>
<div>
<img src="data/Overall_Cropped.svg" width="100%">
</div>
<div style="position:absolute;left:785px;top:137px;">
<video src="data/movies/cutting_noocc_result_png.mp4" height="50%" loop class="slideautostart"></video>
</div>
</section>
<!-- SEQUENTIAL POINT CLOUDS -->
<section>
<section>
<h2>Sequential Clouds & Occlusion Reasoning </h2>
<p> Occlusions appear as “shadows” in rendered point clouds. </p>
<p> For instance, here the lemon (which we want to keep track of) and much of the table is hidden by the bowl.</p>
<video src="data/movies/occlusion_demo.mp4" height="50%" controls loop class="slideautostart"></video>
<p> These blank areas limit our ability to have temporal continuity - object permanence.</p>
</section>
<section>
<h2> Pointcloud <em>without</em> Occlusion Reasoning </h2>
<iframe src="http://pointclouds.org/assets/viewer/pcl_viewer.html?load=http://134.76.92.76/data/occlusion_without.pcd&psize=3" width="1100" height="750" marginwidth="0" marginheight="0" frameborder="no" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="" style="max-width: 100%;">
</iframe>
<p>Fortunately, we can perform some <span class="fragment highlight-green">low-level reasoning</span> about occlusions.</p>
</section>
</section>
<section>
<section>
<h2>Sequentially Updated Octree <cite></cite> </h2>
<p> If we assume no camera motion, we can reason about why voxels “disappear” </p>
<p> Check for occlusion by ray-tracing paths from voxel to camera</p>
<video src="data/movies/occlusion_manip1_other_input_centroids.mp4" width="100%" controls loop class="slideautostart"></video>
<p> Camera is <strong style="color:red">facing us</strong> from this perspective - notice shadows extend towards the viewer.</p>
<br><br>
<div class='citation'>
<footl>
<footi>Papon et al., <a href="http://www.jeremiepapon.com/iros-2013-video-segmentation/">Point Cloud Video Object Segmentation using a Persistent Supervoxel World-Model,</a> Intelligent Robots and Systems (IROS) 2013.</footi>
</footl>
</div>
</section>
<section>
<h2> Demonstration of Occlusion Reasoning </h2>
<p> Left frame shows input data without occlusion reasoning </p>
<video src="data/movies/occlusion_comparison.mp4" width="100%" controls loop class="slideautostart"></video>
<p> Right shows the same input with ray-tracing checks </p>
</section>
<section>
<h2> Pointcloud <em>with</em> Occlusion Reasoning </h2>
<iframe src="http://pointclouds.org/assets/viewer/pcl_viewer.html?load=http://134.76.92.76/data/occlusion_with.pcd&psize=3" width="1100" height="750" marginwidth="0" marginheight="0" frameborder="no" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="" style="max-width: 100%;">
</iframe>
</section>
</section>
<section>
<h2>Overview of Methodology</h2>
<div>
<img src="data/Overall_Cropped.svg" width="100%">
</div>
<div style="position:absolute;left:785px;top:137px;">
<video src="data/movies/cutting_noocc_result_png.mp4" height="50%" loop class="slideautostart"></video>
</div>
</section>
<!-- PARTICLE FILTERING -->
<section>
<h2>Particle filter tracking in Point Clouds</h2>
<p> Correspondence-Based Particle Filter approach is used. </p>
<figure class="embed hide-smooth dark" >
<div style="width:100%">
<img src="data/TideModelSV.svg">
<figcaption style="font-size:1.0em">
Models used for tracking are point clouds, partitioned using supervoxels into strata for sampling.
</figcaption>
</div>
</figure>
</section>
<section>
<h2>Stratified Correspondence Sampling <cite></cite></h2>
<figure class="embed hide-smooth dark" >
<div style="width:100%">
<img src="data/StratifiedCorrespondences.svg">
<figcaption style="font-size:1.0em">
Supervoxels are used to choose spatial strata for uniform random sampling.
</figcaption>
</div>
</figure>
<br><br>
<div class='citation'>
<footl>
<footi>Papon et al., <a href="http://www.jeremiepapon.com/wacv-2015-tracking/">Spatially Stratified Correspondence Sampling for Real-Time Point Cloud Tracking,</a> Applications of Computer Vision (WACV), 2015.</footi>
</footl>
</div>
</section>
<section>
<section>
<h2> Results in Real Application </h2>
<video src="data/movies/Demo4Fast.mp4" height="100%" controls loop class="slideautostart"></video>
<p class="rcred"><a href="http://www.intellact.eu/">IntellAct Project</a></p>
</section>
<section>
<h2> Results in Real Application </h2>
<video src="data/movies/humandemo.mp4" height="100%" controls loop class="slideautostart"></video>
<p class="rcred"><a href="http://www.intellact.eu/">IntellAct Project</a></p>
</section>
</section>
<section>
<section>
<h2> Results on Synthetic Benchmark <cite></cite> </h2>
<div align="center">
<video src="data/movies/milk_small.mp4" height="100%" controls loop class="slideautostart"></video>
</div> <br>
<div class='citation'>
<footl>
<footi>Choi and Christensen,<a href="http://www.cc.gatech.edu/~cchoi/rgbd_obj_tracking.html" “RGB-D> Object Tracking: A Particle Filter Approach on GPU,</a> International Conference on Intelligent Robots and Systems (IROS), 2013.
</footi>
</footl>
</div>
</section>
<section>
<h2> Results on Synthetic Benchmark </h2>
<div align="center">
<video src="data/movies/Tide_small.mp4" height="100%" controls loop class="slideautostart"></video>
</div>
</section>
<section>
<h2> Results on Synthetic Benchmark </h2>
<div align="center">
<video src="data/movies/kinect.mp4" height="100%" controls loop class="slideautostart"></video>
</div>
</section>
</section>
<section>
<section>
<h2>Results on Synthetic Benchmark</h2>
<p>
<figure class="embed-top reveal-smooth dark" >
<div style="width:100%">
<img src="data/AngularErrorOrangeJuice.svg">
<figcaption style="font-size:1.0em">
Plot of Displacement Error vs time per frame (ms) averaged across 50 VR Test Runs for different numbers of particles and samples per stratum.
</figcaption>
</div>
</figure>
</section>
<section>
<h2>Results on Synthetic Benchmark</h2>
<figure class="embed-top reveal-smooth dark" >
<div style="width:100%">
<img src="data/DispErrorMilk.svg">
<figcaption style="font-size:1.0em">
Plot of Rotational Error vs time per frame (ms) averaged across 50 VR Test Runs for different numbers of particles and samples per stratum.
</figcaption>
</div>
</figure>
</section>
</section>
<!-- HIERARCHICAL SUPERVOXEL TRACKING -->
<section>
<h2> Tracking Low Level Patches - <br>Why Temporal Supervoxels? </h2>
<p> Tracking low level patches would let us make temporal connections <span class="fragment highlight-green"> <em>without</em> needing to specify a-priori objects.</p>
<figure class="embed hide-smooth dark" >
<img src="data/splitting_objects.svg" >
<figcaption style="font-size:1.0em">
Splitting objects are problematic if we segment and track using a-priori models. <br> How do we label the pieces?
</figcaption>
</figure>
<p class="fragment"> We have our low level patch representation - Supervoxels. </p>
<p class="fragment"> We have en efficient tracking method. </p>
<p class="fragment" style="font-size:150%; text-align:center"> So, what's the problem? </p>
</section>
<section>
<h2> Why can't we just track Supervoxels? </h2>
<p> Cannot track exclusively at low-level due to the “aperture problem” <cite></cite></p>
<embed src="data/flash/twosquares.swf" width="90%" height="70%"></embed>
<p class="rcred"><a href="http://web.mit.edu/persci/demos/Motion&Form/demos/download.html">MIT Perceptual Science Group</a></p>
<br>
<div class='citation'>
<footl>
<footi>McDermott, et al., <a href="http://web.mit.edu/jhm/www/Pubs/McDermott_Weiss_Adelson_2001_motion_form_beyond_junctions.pdf">Beyond junctions: Nonlocal form contraints on motion interpretation.</a> <em>Perception </em> 2001.</footi>
</footl>
</div>
</section>
<!--section>
<h2>Cortical Feedback Mechanisms</h2>
<p>Humans appear to use top-down feedback mechanisms <cite></cite> </p>
<p>Feedback allows high-level areas to influence low-level vision, even receptive fields </p>
<div class="ctr" style="height:80%; width:70%">
<figure class="embed hide-smooth dark" >
<img src="data/feedback_connections.jpg" >
<figcaption style="font-size:1.0em">
Feed-forward and Feedback Mechanisms in the Human Visual Cortex
</figcaption>
</figure>
</div>
<br>
<div class='citation'>
<footl>
<footi> Gilbert and Wu Li. <a href="http://www.nature.com/nrn/journal/v14/n5/full/nrn3476.html">Top-down influences on visual processing,</a> <em>Nature Reviews Neuroscience,</em> 2013.</footi>
</footl>
</div>
</section-->
<section>
<h2> Hierarchical Temporal (super)Voxel Fields (HTVF) </h2>
<div id="Stage" class="EDGE-3489513"></div>
</section>
<section>
<section>
<h2> HTVF - Cutting Video 0</h2>
<video src="data/movies/cutting_render_result.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<h2> HTVF - Cutting Video 1</h2>
<video src="data/movies/cutting_input_result.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
</section>
<section>
<h2> HTVF - Occlusions - Without Voxel Raytracing </h2>
<video src="data/movies/occlusion1_none_input_result.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<section>
<h2> HTVF - Occlusions - With Voxel Raytracing 0</h2>
<video src="data/movies/occlusion_manip1_other_input_rendering.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<h2> HTVF - Occlusions - With Voxel Raytracing 1 </h2>
<video src="data/movies/occlusion_manip1_input_result.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<h2> HTVF - Occlusions - With Voxel Raytracing 2 </h2>
<video src="data/movies/occlusion_manip1_other2_input_voxels.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<h2> HTVF - Occlusions - With Voxel Raytracing 3 </h2>
<video src="data/movies/occlusion_manip1_other_input_voxels.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
<section>
<h2> Occlusions - Just Occlusion Filling </h2>
<video src="data/movies/occlusion_manip1_other_input_centroids.mp4" height="100%" controls loop class="slideautostart"></video>
</section>
</section>
<!--
+++++++++++++++++++++
MACHINE LEARNING STUFF
+++++++++++++++++++++
-->
<section>
<h2> Current Work - Estimating Pose using deep CNNs </h2>
<p> Generic 6DoF Pose Estimation is a <em>hard</em> problem </p>
<ul>
<li class="fragment">Baseline methods work by finding feature or point correspondences (e.g. ICP)</li>
<li class="fragment">Much effort has been spent designing model-based approaches for particular classes, but this is time consuming.</li>
</ul>
<figure class="embed hide-smooth dark" >
<img src="data/bunny.png" >
<figcaption style="font-size:1.0em">
Can we avoid needing to design features for correspondences, and learn them instead?
</figcaption>
</figure> <br>
<p class="fragment">Unfortunately, generic pose is very difficult to get training data for.</p>
<p class="fragment" style="font-size:150%; text-align:center">Fortunately, we can synthesize it!</p>
</section>
<section>
<h2>Synthetic Dataset</h2>
<ul>
<li class="fragment">Generate data with random poses so we can have accurate ground truth</li>
<li class="fragment">Use meshes from Princeton ModelNet <cite></cite> - for now, chairs</li>
<br>
<li class="fragment">Why Chairs?</li>
<div class="fragment" >
<ul style="list-style-type: none">
<li>Lots of them, aligned with canonical pose</li>
<li>Can limit degrees of freedom initially</li>
<li>Lots of intra-class variability, but can still generalize</li>
</ul>
<figure class="embed-bottom reveal-smooth dark" >
<div class="ctr w100">
<img src="data/pose/chairs.png">
<figcaption style="font-size:1.0em">
Chairs. Lots of Chairs.
</figcaption>
</div>
</figure>
</div>
</ul>
<div class='citation'>
<footl>
<footi>Wu, et al. <a href="http://arxiv.org/abs/1406.5670">"3D ShapeNets for 2.5D Object Recognition and Next-Best-View Prediction"</a> <em> arXiv </em> 2014.</footi>
</footl>
</div>
</section>
<section>
<h2>Synthetic Data</h2>
<p> Generate data using Blensor Python API and a custom Kinect Sensor Model</p>
<p> Basic "Room" with 7 randomly (not touching) placed chairs.</p>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/blender.png">
</div>
</figure>
</section>
<section>
<section>
<h2>Synthetic Data - Depth</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_depth.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic Data - Depth</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/1_depth.png">
</div>
</figure>
</section>
</section>
<section>
<section>
<h2>Synthetic Data - Normals</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_nx.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic Data - Normals</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_ny.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic Data - Normals</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_nz.png">
</div>
</figure>
</section>
</section>
<section>
<section>
<h2>Synthetic Data - Labels</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_labels.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic Data - Labels</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/1_labels.png">
</div>
</figure>
</section>
</section>
<section>
<section>
<h2>Synthetic vs Real</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_depth.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic vs Real</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/real_depth.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic vs Real</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_nx.png">
</div>
</figure>
</section>
<section>
<h2>Synthetic vs Real</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/real_nx.png">
</div>
</figure>
</section>
</section>
<section>
<section>
<h2>Signatures - Depth</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/depth_sigs.png">
</div>
</figure>
</section>
<section>
<h2>Signatures - nx</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/nx_sigs.png">
</div>
</figure>
</section>
<section>
<h2>Signatures - ny</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/ny_sigs.png">
</div>
</figure>
</section>
<section>
<h2>Signatures - nz</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/nz_sigs.png">
</div>
</figure>
</section>
</section>
<section>
<section>
<h2>LCCP to Generate Proposals</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_lccp_boxes.svg">
</div>
</figure>
</section>
<section>
<h2>Synthetic Data - Labels</h2>
<figure class="embed-top reveal-smooth dark" >
<div class="ctr w80">
<img src="data/pose/0_labels.png">
</div>