A Novel Partial 3D Object Retrieval Method Using Adaptive Slices Clustering

Taybi, Ilyass Ouazzani; Bouksim, Mohcine; Alaoui, Rachid; Gadi, Taoufiq

doi:10.22266/ijies2019.0228.25

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The traditional hand-crafted feature-based approaches can be broadly divided into two groups [9] [10]: 2D image-based approaches [11] [12] [13] [14] and model-based approaches [15] [16] [17].…”

Section: A Hand-crafted Feature-based Approachesmentioning

confidence: 99%

2DSlicesNet: A 2D Slice-Based Convolutional Neural Network for 3D Object Retrieval and Classification

2021

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3D data can be instrumental to the computer vision field as it provides insightful information about the full 3D models' geometry. Recently, with easy access to both computational power and huge 3D databases, it is feasible to apply convolutional neural networks to automatically extract the 3D models' features. This paper presents a novel approach, called 2DSlicesNet, which deals with the issue of 3D model retrieval and classification using a 2D slice-based representation with a 3D convolutional neural network.The assumption in this context is that similar 3D models will be composed of almost identical 2D slices. Therefore, we first transform each normalized 3D model into a set of 2D slices corresponding to its first main axis, and then use them as input data to our 3D convolutional neural network. Experimental results and comparison with state-of-the-art approaches, using ModelNet10 and ModelNet40 datasets, prove that our proposed 2DSlicesNet approach can reach notable rates of accuracy in classification and retrieval.INDEX TERMS Deep learning, 2D slices, 3D Convolutional neural network, 3D object classification, 3D object retrieval.

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Section: A Hand-crafted Feature-based Approachesmentioning

confidence: 99%

2DSlicesNet: A 2D Slice-Based Convolutional Neural Network for 3D Object Retrieval and Classification

2021

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“…Otherwise, the clustering step generates over-partition or under-partition. In order to remedy this problem the authors in [16] used a cluster validity index to adapt the number of clusters to the complexity of each 3D object.…”

Section: B 2d Image Based Approachesmentioning

confidence: 99%

A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

Taybi¹,

Gadi²,

Alaoui³

2020

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This paper examines the issue of 3D object indexing and retrieval and tries to solve this problem using partial indexing approach. The hypothesis in this context is that similar 3D objects will be composed of similar 2D slices. The proposed partial 3D object indexing and retrieval method is applicable on both complete and incomplete 3D objects, which is based on a similarity measuring between 2D slices of 3D objects. The main idea behind our approach is to extract an initial set of 2D slices corresponding to determined axes, and then use the Apriori algorithm to select the most representative ones, transforming the issue of shapematching between 3D objects into evaluating the similarities between their 2D slices. Experiments on the Princeton Shape Benchmark (PSB) indicate that our approach outperforms evaluated retrieval approaches.

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A Novel Partial 3D Object Retrieval Method Using Adaptive Slices Clustering

Cited by 2 publications

References 21 publications

2DSlicesNet: A 2D Slice-Based Convolutional Neural Network for 3D Object Retrieval and Classification

2DSlicesNet: A 2D Slice-Based Convolutional Neural Network for 3D Object Retrieval and Classification

A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

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