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<p style="margin:0cm"><span style="font-family:"Verdana",sans-serif">We would like to draw your attention to the "</span>
<span style="font-family:"Verdana",sans-serif">Call for Papers - Advances on Point Cloud technology: From coding and quality evaluation to applications”, issued by the EURASIP Journal of Image and Video Processing. Further details may be found below and here:<o:p></o:p></span></p>
<p style="margin:0cm"><a href="https://www.springeropen.com/collections/apct">Call for Papers - Advances on Point Cloud technology: From coding and quality evaluation to applications (springeropen.com)</a><o:p></o:p></p>
<p style="margin:0cm"><o:p> </o:p></p>
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<span style="font-size:24.0pt;font-family:"Georgia",serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">Call for Papers - Advances on Point Cloud technology: From coding and quality evaluation to applications<o:p></o:p></span></p>
<p class="MsoNormal" style="background:#FCFCFC"><b><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">Guest Editors:</span></b><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU"><br>
<br>
<b>António Pinheiro:</b> Universidade da Beira Interior, Portugal<br>
<b>Ricardo de Queiroz: </b>Universidade Federal de Brasilia, Brasil<br>
<b>Stuart Perry:</b> University of Technology Sydney, Australia<br>
<b>Luís Cruz: </b>Instituto de Telecomunicações, Universidade de Coimbra, Portugal<br>
<b>Irene Viola: </b>Centrum Wiskunde & Informatica, Netherlands<o:p></o:p></span></p>
<p class="MsoNormal" style="background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Georgia",serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">Submission Status: Open<i> </i>| Submission Deadline: 30 November 2023</span><span style="font-size:13.5pt;font-family:"Georgia",serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU"><br>
<i><img border="0" width="685" height="300" style="width:7.1354in;height:3.125in" id="Picture_x0020_2" src="cid:image001.jpg@01D9F06B.C19C8E90"></i><o:p></o:p></span></p>
<p class="MsoNormal" style="background:#FCFCFC"><i><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU"><a href="https://jivp-eurasipjournals.springeropen.com/" target="_blank"><span style="color:#004B83">EURASIP
Journal on Image and Video Processing</span></a></span></i><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU"> is calling for submissions to our Collection on <i>Advances on Point Cloud
technology: From coding and quality evaluation to applications</i><o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:18.0pt;background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">In this special issue of the EURASIP JIVP, leading researchers
and practitioners in academia, industry and standard-bodies are invited to contribute to the advancement of the state of the art on point cloud processing technology, by submitting new coding models based either on the traditional approaches or in machine
learning technology, new quality models for subjective quality estimation, new objective quality measures, and new methods and applications of point cloud processing.<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:18.0pt;background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">Point Clouds (PC) are one of the formats being considered to represent
plenoptic information. They provide a flexible representation of 3D visual information with multiple applications to virtual, mixed and augmented reality, computer graphics modelling, medical imaging, mobile robot environment modelling for use in autonomous
driving and other applications. <o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:18.0pt;background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">However, the widespread adoption of point cloud applications faces
several challenges, amongst which the most pressing one is the volume of data required to represent the geometry and attributes of the increasingly larger sets of points generated by point cloud capture and generation processes and devices. <o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:18.0pt;background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">Solving this problem calls for advanced coding models able to reduce
the data size of point clouds to volumes compatible with efficient transmission, storage and processing. <o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:18.0pt;background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">In the last few years several point cloud coding methods have been
proposed based on different representation principles, from octree decompositions to projection onto 2D images followed by waveform coding via legacy image coders. Recently a new class of methods leveraging the power of (deep) machine learning has been proposed,
achieving good performance in geometry information coding, that is being followed by integrating machine learning based attribute compression tools. Besides the prospects of achieving coding performance (geometry and attributes) exceeding the state-of-art
performance the machine learning solutions also permit direct compressed domain processing, with significant savings in the computational costs associated with decoding operations.<o:p></o:p></span></p>
<p class="MsoNormal" style="background:#FCFCFC"><span style="font-size:18.0pt;font-family:"Georgia",serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">About the collection</span><span style="font-size:18.0pt;font-family:"Georgia",serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU"><o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:18.0pt;background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">Prospective authors are encouraged to submit unpublished manuscripts
for publication in this Special Issue. Subject of interests include, but are not limited to: <o:p></o:p></span></p>
<p class="MsoNormal" style="background:#FCFCFC"><span style="font-size:13.5pt;font-family:"Segoe UI",sans-serif;color:#333333;mso-ligatures:none;mso-fareast-language:EN-AU">• Machine learning based point cloud coding<br>
• Point cloud coding with standardized codecs<br>
• Point cloud content analysis and processing<br>
• Point clouds subjective quality evaluation<br>
• Objective quality metrics for point clouds<br>
• Large public and annotated test and training data sets with descriptions<br>
• New methods for acquisition and generation of point clouds<br>
• Point cloud pre- and post-processing, for efficient coding<br>
• Point cloud super-resolution methods<br>
• Point cloud denoising methods<br>
• Novel applications of point clouds<br>
• Implementation and optimization of point cloud processing and coding methods<br>
• Point Cloud applications in IoT<br>
• Instance and semantic segmentation of the point cloud data<br>
• Impact of compression algorithms on computer vision algorithms such as parts recognition and object classification<o:p></o:p></span></p>
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