Graph-based characteristic view set extraction and matching for 3D model retrieval

Liu, An-An; Wang, Zhongyang; Nie, Weizhi; Su, Yuting

doi:10.1016/j.ins.2015.04.042

Cited by 67 publications

(17 citation statements)

References 54 publications

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“…There are some state of the art datasets that have captured single objects in isolation with real sensors for 3D object recognition, as presented in [27]. RGBD Object Dataset [28] and MV-RED [29] offer captures of 300 and 500 objects using Kinect v1, but without information of the pose. BigBIRD dataset [30] and YCB Object and Model Set [31] additionally incorporate pose information of 100 objects using an Asus Xtion Pro sensor.…”

Section: Related Workmentioning

confidence: 99%

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

Gomez-Donoso

Cazorla

2020

Applied Sciences

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Deep learning-based methods have proven to be the best performers when it comes to object recognition cues both in images and tridimensional data. Nonetheless, when it comes to 3D object recognition, the authors tend to convert the 3D data to images and then perform their classification. However, despite its accuracy, this approach has some issues. In this work, we present a deep learning pipeline for object recognition that takes a point cloud as input and provides the classification probabilities as output. Our proposal is trained on synthetic CAD objects and is able to perform accurately when fed with real data provided by commercial sensors. Unlike most approaches, our method is specifically trained to work on partial views of the objects rather than on a full representation, which is not the representation of the objects as captured by commercial sensors. We trained our proposal with the ModelNet10 dataset and achieved a 78.39 % accuracy. We also tested it by adding noise to the dataset and against a number of datasets and real data with high success.

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Section: Related Workmentioning

confidence: 99%

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

Gomez-Donoso

Cazorla

2020

Applied Sciences

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“…In this area, multi-view approaches are popular recently. For instance, Liu et al [11] proposed to enhance image representations by including information of 3D space. They constructed a view-graph model with spatial information of different views, thus transforming the issue of shape distance measurement into a graph matching problem.…”

Section: Related Workmentioning

confidence: 99%

“…3D model-based methods like [2], [3], [4], [5], [6], [7], [8] extract high-level descriptors directly from the raw representation of 3D shapes. By contrast, view-based methods like [11], [12], [13], [14], [15] aim to extract features from 2D images of a 3D shape. The final shape descriptor is constructed from these view features.…”

Section: Introductionmentioning

confidence: 99%

Learning Discriminative 3D Shape Representations by View Discerning Networks

Leng

Zhang

Xiaocheng

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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In view-based 3D shape recognition, extracting discriminative visual representation of 3D shapes from projected images is considered the core problem. Projections with low discriminative ability can adversely influence the final 3D shape representation. Especially under the real situations with background clutter and object occlusion, the adverse effect is even more severe. To resolve this problem, we propose a novel deep neural network, View Discerning Network, which learns to judge the quality of views and adjust their contribution to the representation of shapes. In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors. These score vectors are used to weight the image features and the weighted features performs much better than original features in 3D shape recognition task. In particular, we introduce two structures of Score Generation Unit, Channel-wise Score Unit and Part-wise Score Unit, to assess the quality of feature maps from different perspectives. Our network aggregates features and scores in an end-to-end framework, so that final shape descriptors are directly obtained from its output. Our experiments on ModelNet and ShapeNet Core55 show that View Discerning Network outperforms the state-of-the-arts in terms of the retrieval task, with excellent robustness against background clutter and object occlusion.

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“…Therefore, the retrieval technique depends strongly on the feature extraction approach used. These approaches can be divided into geometry based approaches [4], and 2D based ones [5].…”

Section: Introductionmentioning

confidence: 99%

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

Taybi¹,

Bouksim²,

Alaoui³

et al. 2019

IJIES

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The similarity measuring between 3D objects can be reduced to a distance between their descriptors. Therefore, the principal challenge is the mapping of objects into reduced and compact representations referred to as descriptors. Generally speaking, descriptors based on global features cannot be effective to describe incomplete 3D objects. This paper presents a novel partial 3D object retrieval approach called adaptive slices clustering (ASC). To ensure that similar objects will be decomposed similarly, normalization is applied to the objects as a preprocessing step. Then, we characterize each object by a set of 2D slices corresponding to its three principal axes, transforming the shape-matching problem between objects into similarity measuring between their slices. Next, we choose among the extracted 2D slices the most representative to reduce searching time without diminishing the performance of our approach. The results show that our approach provides better performance than the descriptors that characterize the whole object.

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Graph-based characteristic view set extraction and matching for 3D model retrieval

Cited by 67 publications

References 54 publications

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

Learning Discriminative 3D Shape Representations by View Discerning Networks

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

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