Special Issue "Sensors and Computer Vision Techniques for 3D Object Modeling"
Deadline for manuscript submissions: closed (15 December 2020).
Interests: computer vision; machine learning; robotics; artificial intelligence
Special Issues and Collections in MDPI journals
Perceiving and predicting the complex 3D structure of the dynamic world requires a combination of sophisticated geometric modeling, efficient learning abilities, as well as having the right 3D sensors and imaging devices. Today’s research in robotics, computer vision, 3D sensing, and machine learning offers a plethora of fast algorithms, accurate 3D sensing capabilities, as well as powerful deep neural networks, which leverage the statistical properties of the moving 3D world. This Special Issue aims to bring together state-of-the-art research in all these directions for 3D learning, modeling, and prediction to establish a strong common foundation and create bridges towards next-generation models and methods.
Topics of interest include but are not limited to the following:
- Advanced sensors and analytical techniques for 3D object modeling and depth estimation;
- Deep learning and computer vision approaches to 3D object recognition, depth, pose and trajectory estimation in static or dynamic scenes with multiple objects;
- Efficient algorithms and computational models for 3D robot perception, mapping, localization, obstacle avoidance, navigation, and semantic reasoning;
- 3D scene estimation for self-driving cars and autonomous aerial vehicles;
- Sensor fusion and multitask learning for 3D modeling and estimation;
- 3D object modeling and prediction on embedded platforms;
- Unsupervised learning of depth and 3D structures in space and time;
- Active reinforcement learning approaches to 3D perception and prediction;
- Sensors, techniques, and advanced machine learning models for 3D learning, modeling, and estimation with applications to medicine, environment, agriculture, transportation, aerospace, civil structures, and different other industries;
- Machine Learning Techniques for Augmented and Virtual Reality;
- Efficient Learning and Computation for Embedded Vision Systems.
Assoc. Prof. Marius Leordeanu
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.