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Applied Sciences
Special Issues
Applications of Computer Vision in 3D Perception
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Applications of Computer Vision in 3D Perception

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 2409

Special Issue Editor

Dr. Yang Li

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China.
Interests: visual depth perception; 3D reconstruction; bio-image informatics

Special Issue Information

Dear Colleagues,

As robots and autonomous systems deviate from laboratory setups towards complex real-world scenarios, both the 3D perception capabilities of these systems and their abilities to acquire and model 3D semantic information must become more powerful. This Special Issue focuses on a broad variety of topics in the area of computer vision in 3D perception, from novel 3D imaging methods, optical sensors, signal processing, geometric modeling, representation, and transmission to visualization, analysis, interaction, and a variety of applications.

ACQUISITION

  • Geometry
  • Calibration
  • Physics-based vision and shape from X
  • Structure from motion and SLAM
  • Dense reconstruction and stereo
  • Structured light and TOF sensors

MODELLING

  • Shape representation and features
  • Geometry processing
  • Appearance modeling
  • Registration
  • Generative and morphable models
  • 3D motion modeling
  • High-level representation of 3D data

ANALYSIS

  • Shape recognition and analysis
  • Segmentation
  • Motion and tracking
  • Body, face, and gesture
  • Dataset and benchmarking

APPLICATIONS

  • Robotics
  • Industrial
  • Space
  • Medical
  • Entertainment
  • Sports
  • Biology

Dr. Yang Li
Guest Editor

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 submissions that pass pre-check are 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. Applied Sciences 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 2400 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.

Published Papers (2 papers)

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Research

14 pages, 9099 KiB  
Article
CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
by Pinzhi Wang, Ming Li, Jinghao Cao, Sidan Du and Yang Li
Appl. Sci. 2024, 14(2), 517; https://doi.org/10.3390/app14020517 - 06 Jan 2024
Viewed by 741
Abstract
Estimating 360∘ depth from multiple cameras has been a challenging problem. However, existing methods often adopt a fixed-step spherical sweeping approach with densely sampled spheres and use numerous 3D convolutions in networks, which limits the speed of algorithms in practice. Additionally, obtaining [...] Read more.
Estimating 360∘ depth from multiple cameras has been a challenging problem. However, existing methods often adopt a fixed-step spherical sweeping approach with densely sampled spheres and use numerous 3D convolutions in networks, which limits the speed of algorithms in practice. Additionally, obtaining high-precision depth maps of real scenes poses a challenge for the existing algorithms. In this paper, we design a cascade architecture using a dynamic spherical sweeping method that progressively refines the depth estimation from coarse to fine over multiple stages. The proposed method adaptively adjusts sweeping intervals and ranges based on the predicted depth and the uncertainty from the previous stage, resulting in a more efficient cost aggregation performance. The experimental results demonstrated that our method achieved state-of-the-art accuracy with reduced GPU memory usage and time consumption compared to the other methods. Furthermore, we illustrate that our method achieved satisfactory performance on real-world data, despite being trained on synthetic data, indicating its generalization potential and practical applicability. Full article
(This article belongs to the Special Issue Applications of Computer Vision in 3D Perception)
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22 pages, 28670 KiB  
Article
CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
by Yuanfeng Lian and Mengqi Chen
Appl. Sci. 2023, 13(5), 3261; https://doi.org/10.3390/app13053261 - 03 Mar 2023
Viewed by 1201
Abstract
3D non-rigid shape correspondence is significant but challenging in computer graphics, computer vision, and related fields. Although some deep neural networks have achieved encouraging results in shape correspondence, due to the complexity of the local deformation of non-rigid shapes, the ability of these [...] Read more.
3D non-rigid shape correspondence is significant but challenging in computer graphics, computer vision, and related fields. Although some deep neural networks have achieved encouraging results in shape correspondence, due to the complexity of the local deformation of non-rigid shapes, the ability of these networks to identify the spatial changes of objects is still insufficient. In this paper, we design a Component-aware Capsule Graph Network (CA-CGNet) to further address the features of embedding space based on the component constraints. Specifically, the dynamic clustering strategy is used to classify the features of patches produced by over-segmentation in order to further reduce noise interference. Moreover, aiming at the problem that existing routing ignores the embedding relationship between capsules, we propose a component-aware capsule graph routing to fully describe the relationship between capsules, which regards capsules as nodes in the graph network and constrains nodes through component information. Then, a knowledge distillation strategy is introduced to improve the convergence speed of the network by decreasing the parameters while maintaining accuracy. Finally, a component pair constraint is added to the functional map, and the component-based semantic loss function is proposed, which can compute isomeric in both direct and symmetric directions. The experimental results show that CA-CGNet improves by 10.21% compared with other methods, indicating the accuracy, generalization, and efficiency of our method on the FAUST, SCAPE, TOSCA, and KIDS datasets. Full article
(This article belongs to the Special Issue Applications of Computer Vision in 3D Perception)
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