Lang Zhou , Guoxing Sun , Yong Li , Weiqing Li , Zhiyong Su
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引用次数: 0
Abstract
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of point cloud denoising techniques. In this article, we first provide a comprehensive survey on state-of-the-art denoising solutions, which are mainly categorized into three classes: filter-based, optimization-based, and deep learning-based techniques. Methods of each class are analyzed and discussed in detail. This is done using a benchmark on different denoising models, taking into account different aspects of denoising challenges. We also review two kinds of quality assessment methods designed for evaluating denoising quality. A comprehensive comparison is performed to cover several popular or state-of-the-art methods, together with insightful observations. Finally, we discuss open challenges and future research directions in identifying new point cloud denoising strategies.
期刊介绍：
Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics.
We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way).
GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.
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