Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval

Naffouti, Seif Eddine; Fougerolle, Yohan; Aouissaoui, Ichraf; Sakly, Anis; Mériaudeau, Fabrice

doi:10.1007/s11760-018-1235-7

Cited by 6 publications

(4 citation statements)

References 18 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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“…According to the types of features employed, existing general 3D model matching techniques can be divided into four categories: [29] and wave kernel signature [30]. Recently, some matching algorithms based on spectral geometry are proposed for non-rigid and deformable 3D model retrieval [31], [32].…”

Section: A Random Walk Algorithmmentioning

confidence: 99%

Partial Matching of Large Scale Process Plant Models Using Random Walk on Graphs

Mao

Dai

et al. 2020

IEEE Access

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3D process plant models(PPMs) in the process industry normally consists of thousands of components. And, there are many similar local structures in the PPM. Due to the complex process flow, the topology relationship among components is very complicated. Therefore, designing a new PPM is quite time consuming. In order to shorten the design cycle, content based model retrieval for PPMs is an imperative requirement. In this paper, we propose a partial matching framework for PPMs based on graph matching aiming at improving design efficiency and realizing design reuse. The random walk algorithm is employed to distinguish similar local structures. Specifically, each PPM is represented by an undirected labeled graph. The local topological feature of each component is extracted based on the random walk algorithm. For partial matching, a subgraph isomorphism algorithm is introduced. The matching process is accelerated by using the local topological feature to generate an optimized initial state and alleviate the computation of feasible rules. Experimental results show the feasibility and effectiveness of our matching framework.

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“…''#'' is represented to the total number. precision = # correct descriptor matches #descriptor matches (5) recall = #correct descriptor matches # corresponding keypoint pairs (6) When the distance ||q j − Tp i || between the keypoint p i in a model and the keypoint q j in a scene is smaller than a threshold s, the p i and q j is considered as a corresponding keypoint pair (T is the truth transformation matrix). A descriptor match is considered as correct when the corresponding descriptors' keypoints are corresponding keypoint pairs [3].…”

Section: B Criterionmentioning

confidence: 99%

“…In recent years, artificial intelligence (AI) technologies, which is mainly centered on robots and pattern recognition, have been greatly studied with the development in the data acquisition technology, computer processing capacity and the accumulation of big data [1]. As one of the fundamental but challenging issue in computer vision, robotic and remote sensing [2], [3], 3D surface matching by local feature descriptors has been widely used in various applications [4], for instance, 3D object registration [5], 3D model retrieval [6], [7], 3D object recognition [8], [9], and 3D model reconstruction [10], [11].…”

Section: Introductionmentioning

confidence: 99%

3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

Zhou

Jiang

2019

IEEE Access

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3D surface matching by local feature descriptors is a fundamental task in 3D object registration, recognition, and retrieval. In view of 2D projected descriptors' information compression and 3D directly voxelized descriptors' sensitivity to density variation and boundary, this paper first proposes a voxel-based buffer-weighted binary descriptor, named VBBD. After local surfaces of detected keypoints are voxelized, each voxel's buffer region is established, and the buffer-weighted Gaussian kernel density is calculated. If the current voxel's buffer-weighted density is larger than its local surface's average density, the voxel is labeled 1, otherwise, it is 0. The proposed descriptor has several merits: 1) direct acquisition of 3D information without projection, the neighbor information is less compressed. 2) voxels are labeled and binarized according to the buffer-weighted density, which improves the robustness to boundary effect, noise and density variation. Based on the VBBD, a global optimal surface matching method based on a Kuhn_Munkres (KM) algorithm is adopted. The strategy has several advantages: 1) by calculating the descriptors of large-scaled surfaces which are down sampled, the number of the point cloud is reduced, and meanwhile, large-scaled information is obtained; and 2) KM algorithm is adopted to find matching pairs to achieve final maximum weight sum of all matching pairs, which can efficiently avoid local optimum. The experimental results show that, compared with other state-of-the-art descriptors, the VBBD has better descriptiveness and robustness, and the surface matching strategy by the VBBD can achieve both high recall and precision.INDEX TERMS Voxel, Gaussian kernel density, binary descriptor, Kuhn_Munkres matching.

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Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval

Cited by 6 publications

References 18 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

Partial Matching of Large Scale Process Plant Models Using Random Walk on Graphs

3D Surface Matching by a Voxel-Based Buffer-Weighted Binary Descriptor

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