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Applied Sciences
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Technical Advances in 3D Reconstruction
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Technical Advances in 3D Reconstruction

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

Deadline for manuscript submissions: 20 January 2025 | Viewed by 1353

Special Issue Editor

Dr. Xi Zhao

E-Mail Website
Guest Editor
School of Computer Science, Xi'an Jiaotong University, Xi'an 710049, China
Interests: 3D reconstruction; point cloud analysis; 3D content generation; interaction analysis; augmented reality

Special Issue Information

Dear Colleagues,

The task of 3D reconstruction involves creating 3D content or a representation of 3D content from 2D images or other data sources. With the development of deep learning techniques, implicit representations such as Nerf have attracted a lot of attention. Gaussian splatting has also become a popular new 3D representation. This Special Issue aims to present recent findings on the topic of 3D reconstruction and provide us with a fresh outlook on reconstruction-related tasks.

Potential topics include, but are not limited to, the following:

  • Point cloud reconstruction;
  • 3D scene completion;
  • 3D reconstruction from images or videos ;
  • 3D room layout generation;
  • Garment reconstruction;
  • 3D human pose estimation;
  • 3D wireframe reconstruction;
  • 3D shape representations.

Dr. Xi Zhao
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.

Keywords

  • 3D reconstruction
  • 3D content generation
  • shape representation

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Published Papers (2 papers)

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Research

16 pages, 5987 KiB  
Article
From Single Shot to Structure: End-to-End Network-Based Deflectometry for Specular Free-Form Surface Reconstruction
by M.Hadi Sepanj, Saed Moradi, Amir Nazemi, Claire Preston, Anthony M. D. Lee and Paul Fieguth
Appl. Sci. 2024, 14(23), 10824; https://doi.org/10.3390/app142310824 - 22 Nov 2024
Abstract
Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel [...] Read more.
Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender leading to a dataset which we will make available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. Full article
(This article belongs to the Special Issue Technical Advances in 3D Reconstruction)
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21 pages, 7110 KiB  
Article
Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
by Xi Zhao, Yuekun Zhang and Yaqing Zhou
Appl. Sci. 2024, 14(20), 9355; https://doi.org/10.3390/app14209355 - 14 Oct 2024
Viewed by 771
Abstract
For the reconstruction of objects during hand–object interactions, accurate pose estimation is indispensable. By improving the precision of pose estimation, the accuracy of the 3D reconstruction results can be enhanced. Recently, pose tracking techniques are no longer limited to individual objects, leading to [...] Read more.
For the reconstruction of objects during hand–object interactions, accurate pose estimation is indispensable. By improving the precision of pose estimation, the accuracy of the 3D reconstruction results can be enhanced. Recently, pose tracking techniques are no longer limited to individual objects, leading to advancements in the reconstruction of objects interacting with other objects. However, most methods struggle to handle incomplete target information in complex scenes and mutual interference between objects in the environment, leading to a decrease in pose estimation accuracy. We proposed an improved algorithm building upon the existing BundleSDF framework, which enables more robust and accurate tracking by considering the occlusion relationships between objects. First of all, for detecting changes in occlusion relationships, we segment the target and compute dual-layer masks. Secondly, rough pose estimation is performed through feature matching, and a keyframe pool is introduced for pose optimization, which is maintained based on occlusion relationships. Lastly, the estimated results of historical frames are used to train an object neural field to assist in the subsequent pose-tracking process. Experimental verification shows that on the HO-3D dataset, our method can significantly improve the accuracy and robustness of object tracking in frequent interactions, providing new ideas for object pose-tracking tasks in complex scenes. Full article
(This article belongs to the Special Issue Technical Advances in 3D Reconstruction)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Dear Colleagues,

The task of 3D reconstruction involves creating 3D content or a representation of 3D content from 2D images or other data sources. With the development of deep learning techniques, implicit representations such as Nerf have attracted a lot of attention. Gaussian splatting has also become a popular new 3D representation. This Special Issue aims to present recent findings on the topic of 3D reconstruction and provide us with a fresh outlook on reconstruction-related tasks.

Potential topics include, but are not limited to, the following:

  • Point cloud reconstruction;
  • 3D scene completion;
  • 3D reconstruction from images or videos ;
  • 3D room layout generation;
  • Garment reconstruction;
  • 3D human pose estimation;
  • 3D wireframe reconstruction;
  • 3D shape representations.
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