Current Advances in 3D Scene Classification and Object Recognition

Dear Colleagues,

Deep learning algorithms have significantly transformed the landscape of recognition technologies. In contemporary settings, these advanced algorithms excel, often outperforming humans in tasks such as image classification. This process is not only limited to recognizing flat images but extends to more complex operations like pixel-wise classification and object detection, where their performance remains impressive.

However, the scenario shifts when dealing with three-dimensional data. Tasks involving the recognition of objects in range images, depth maps, point clouds, and stereo images introduce unique challenges. Despite substantial progress and dedicated efforts in the field, these challenges are yet to be fully mastered. The complexities of three-dimensional data processing demand innovative approaches and refined algorithms to achieve results comparable to those obtained with two-dimensional data.

In response to these ongoing developments, we are proud to announce a Special Issue entitled "Current Advances in 3D Scene Classification and Object Recognition". This edition is dedicated to exploring groundbreaking and rigorous research that integrates various learning paradigms with three-dimensional data across multiple contexts. This Special Issue aims to highlight novel methodologies and significant advancements in several areas, including, but not limited to, enhanced algorithms for depth perception, improved techniques for processing point clouds, and innovative applications in stereo imaging. This issue will serve as a platform for scholars and practitioners to disseminate their findings and contribute to the evolving field of 3D data analysis.

Specifically, this Special Issue will cover the following topics, among others:

• Deep Learning and machine learning with point clouds;
• Deep Learning and machine learning with depth maps;
• Deep Learning and machine learning with stereo vision;
• Three-dimensional keypoint detectors and descriptors;
• Novel representations of 3D data;
• Three-dimensional data summarization and compression;
• Object detection taking as input any 3D data;
• Point-wise classification taking as input any 3D data.

Dr. Francisco Gomez-Donoso
Dr. Gonzalez-Serrano German
Dr. Félix Escalona Moncholí
Guest Editors

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• 3D scene understanding
• 3D object detection
• 3D data
• point clouds
• depth maps