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Technical Advances in 3D Reconstruction—2nd Edition

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

Deadline for manuscript submissions: 30 December 2026 | Viewed by 1562

Editor


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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 Issues, Collections and Topics in MDPI journals

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 method of 3D representation. This Special Issue aims to present recent findings on the topic of 3D reconstruction to 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 (1 paper)

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Research

20 pages, 11095 KB  
Article
SRNN: Surface Reconstruction from Sparse Point Clouds with Nearest Neighbor Prior
by Haodong Li, Ying Wang and Xi Zhao
Appl. Sci. 2026, 16(3), 1210; https://doi.org/10.3390/app16031210 - 24 Jan 2026
Viewed by 1097
Abstract
Surface reconstruction from 3D point clouds has a wide range of applications. In this paper, we focus on the reconstruction from raw, sparse point clouds. Although some existing methods work on this topic, the results often suffer from geometric defects. To solve this [...] Read more.
Surface reconstruction from 3D point clouds has a wide range of applications. In this paper, we focus on the reconstruction from raw, sparse point clouds. Although some existing methods work on this topic, the results often suffer from geometric defects. To solve this problem, we propose a novel method that optimizes a neural network (referred to as signed distance function) to fit the Signed Distance Field (SDF) from sparse point clouds. The signed distance function is optimized by projecting query points to its iso-surface accordingly. Our key idea is to encourage both the direction and distance of projection to be correct through the supervision provided by a nearest neighbor prior. In addition, we mitigate the error propagated from the prior function by augmenting the low-frequency components in the input. In our implementation, the nearest neighbor prior is trained with a large-scale local geometry dataset, and the positional encoding with a specified spectrum is used as a regularization for the optimization process. Experiments on the ShapeNetCore dataset demonstrate that our method achieves better accuracy than SDF-based methods while preserving smoothness. Full article
(This article belongs to the Special Issue Technical Advances in 3D Reconstruction—2nd Edition)
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