A Survey on Segmentation of 3D Models

He, Chen; Wang, Chunmeng

doi:10.1007/s11277-018-5414-1

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…The hierarchical approach comprises two main techniques: One way is the bottom-up region merging method [37][38][39][40]. This method assigns each face as a chart.…”

Section: Plane Structure Feature Segmentationmentioning

confidence: 99%

A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models

Xiao

Guo

et al. 2020

Remote Sensing

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The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.

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“…The hierarchical approach comprises two main techniques: One way is the bottom-up region merging method [37][38][39][40]. This method assigns each face as a chart.…”

Section: Plane Structure Feature Segmentationmentioning

confidence: 99%

A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models

Xiao

Guo

et al. 2020

Remote Sensing

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“…Segmentation, also referred to as cropping is defined as the process of separating the 3D model into segments that are representative of the model shape, calculated by edge detection. The goal of segmentation is to simplify the representation of a model/mesh into something that is more tangible and easier to analyse [20].…”

Section: Segmentationmentioning

confidence: 99%

Automated Identification from Dental Data (AutoIDD): A new development in digital forensics

Reesu

Woodsend

Mânica

et al. 2020

Forensic Science International

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There has been a significant expansion in the use of 3-dimensional (3D) dental images in recent years. In the field of forensic odontology, an automated 3D dental identification system could enhance the identification process. This study presents a novel method for automated human dental identification using 3D digital dental data by utilising a dental identification scenario. The total study sample was divided into two groups: Group A (120 dental models) and Group B (120 Intra-oral scans-IOS). Group A data was composed of 3D scanned dental models of post-orthodontic treated patients (30 maxillary and 30 mandibular). This data was considered as AM digital data. To generate an identical sample, the dental casts (60) of the same patients were retrieved and laser scanned. These models were considered as PM digital data. Group B data (IOS) was obtained from 30 study participants. To reconstruct a dental identification scenario 30 maxillary and 30 mandibular IOS were obtained from 30 participants and were considered as IOS-AM. After one year, another set of IOS (60) were acquired from the same participants and were considered as IOS-PM. The results showed that the AutoIDD (Automated Identification from Dental Data) software was consistent in accuracy; capable of differentiating "correct matches" (high match percentage) from "non-matches" (very low percentage) by 3D image superimposition. The match percentage of the maxillary and mandibular IOS ranged from 64-100% and 81-100%, with a mean percentage of 96.7 and 96.4 respectively. This study demonstrated the feasibility of using 3D scans through a new automated software-AutoIDD in digital forensics to assist the forensic expert in confirming the identity of a deceased individual from the available AM dental records.

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