A novel sketch-based 3D model retrieval approach based on skeleton

Zhang, Jing; Kang, Bao Sheng; Jiang, Bo; Zhang, Di

doi:10.11591/ijict.v8i1.pp1-12

Cited by 3 publications

(2 citation statements)

References 24 publications

(30 reference statements)

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“…In sketch-based algorithm, 3D model is projected into a set of 2D views. Zhang et al (2017) used PCA-DAISY descriptor and fisher coding algorithm to retrieve 3D model according to 2D sketch. Nie, Wang & Lu (2018) proposed Multi-Scale and Multi-Channel CNN to extract features for 3D model retrieval.…”

Section: Introductionmentioning

confidence: 99%

3D model retrieval based on interactive attention CNN and multiple features

Jia¹,

Zhang²

2023

PeerJ Computer Science

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3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according to freehand sketch. Researchers are focusing on applying machine learning technology to 3D model retrieval. In this article, we combine semantic feature, shape distribution features and gist feature to retrieve 3D model based on interactive attention convolutional neural networks (CNN). The purpose is to improve the accuracy of 3D model retrieval. Firstly, 2D (two-dimensional) views are extracted from 3D model at six different angles and converted into line drawings. Secondly, interactive attention module is embedded into CNN to extract semantic features, which adds data interaction between two CNN layers. Interactive attention CNN extracts effective features from 2D views. Gist algorithm and 2D shape distribution (SD) algorithm are used to extract global features. Thirdly, Euclidean distance is adopted to calculate the similarity of semantic feature, the similarity of gist feature and the similarity of shape distribution feature between sketch and 2D view. Then, the weighted sum of three similarities is used to compute the similarity between sketch and 2D view for retrieving 3D model. It solves the problem that low accuracy of 3D model retrieval is caused by the poor extraction of semantic features. Nearest neighbor (NN), first tier (FT), second tier (ST), F-measure (E(F)), and discounted cumulated gain (DCG) are used to evaluate the performance of 3D model retrieval. Experiments are conducted on ModelNet40 and results show that the proposed method is better than others. The proposed method is feasible in 3D model retrieval.

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Section: Introductionmentioning

confidence: 99%

3D model retrieval based on interactive attention CNN and multiple features

Jia¹,

Zhang²

2023

PeerJ Computer Science

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“…Jing Zhang et al [7] have proposed a new sketch-based 3D model retrieval procedure which employs skeleton characteristics as the features to define the object shape.…”

Section: Introductionmentioning

confidence: 99%

3D model retrieval using MeshSIFT descriptor and fuzzy C-means clustering

Hamza

Jafer

Hadi

2020

IJEECS

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A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The subject of this paper is a shape retrieval of 3D models that are signified as triangle meshes. We propose a new method which first computes the descriptor of 3D models through extracting its features, and then divides a model into clusters depending on a descriptor which is invariant to scale and orientation. A Fuzzy C-means clustering method is utilized for dividing the model into clusters. The superior performance and benefits of our method are shown in the results.

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A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

Yang

Wen

Wang

et al. 2022

Forests

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More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes.

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A novel sketch-based 3D model retrieval approach based on skeleton

Cited by 3 publications

References 24 publications

3D model retrieval based on interactive attention CNN and multiple features

3D model retrieval based on interactive attention CNN and multiple features

3D model retrieval using MeshSIFT descriptor and fuzzy C-means clustering

A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

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