An efficient approach to directly compute the exact Hausdorff distance for 3D point sets

Zhang, Dejun; He, Fazhi; Han, Soonhung; Zou, Lu; Wu, Yonghong; Chen, Yilin

doi:10.3233/ica-170544

Cited by 61 publications

(20 citation statements)

References 78 publications

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“…Fourthly, we also want to extend our method to a hybrid synchronous CAD environment [8]. Finally, we also want to extend the symmetryand consistency-related methods to other areas of science and technology [64][65][66][67][68][69][70][71].…”

Section: Discussionmentioning

confidence: 99%

Collaborative CAD Synchronization Based on a Symmetric and Consistent Modeling Procedure

Han

2017

Symmetry

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Abstract:One basic issue with collaborative computer aided design (Co-CAD) is how to maintain valid and consistent modeling results across all design sites. Moreover, modeling history is important in parametric CAD modeling. Therefore, different from a typical co-editing approach, this paper proposes a novel method for Co-CAD synchronization, in which all Co-CAD sites maintain symmetric and consistent operating procedures. Consequently, the consistency of both modeling results and history can be achieved. In order to generate a valid, unique, and symmetric queue among collaborative sites, a set of correlated mechanisms is presented in this paper. Firstly, the causal relationship of operations is maintained. Secondly, the operation queue is reconstructed for partial concurrency operation, and the concurrent operation can be retrieved. Thirdly, a symmetric, concurrent operation control strategy is proposed to determine the order of operations and resolve possible conflicts. Compared with existing Co-CAD consistency methods, the proposed method is convenient and flexible in supporting collaborative design. The experiment performed based on the collaborative modeling procedure demonstrates the correctness and applicability of this work.

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Section: Discussionmentioning

confidence: 99%

Collaborative CAD Synchronization Based on a Symmetric and Consistent Modeling Procedure

Han

2017

Symmetry

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“…Finally, the output patches are glued together to form the whole hand point cloud. Since the size of the recovered object is not required to be the same as the input in Capsule-HandNet, the Chamfer distance [16] and Hausdorff distance [49] are applicable as the metrics for comparing two shapes in this case. Hence, a symmetric Chamfer distance metric is adopted as the loss function for network optimization to minimize the gap between the recovered object and original point cloud, denoted as…”

Section: Hand Pose Estimation Networkmentioning

confidence: 99%

“…Crossing Nets [49] LSN [51] DeepPrior++ [12] Hierarchical [4] DeepPrior [40] DeepModel [45] LRF [3] So-HandNet [9] Ours Figure 4. Comparisons of fraction of frames on MSRA (left) and ICVL (right) datasets.…”

Section: Icvlmentioning

confidence: 99%

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Zhang

et al. 2020

Symmetry

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Hand pose estimation from 3D data is a key challenge in computer vision as well as an essential step for human–computer interaction. A lot of deep learning-based hand pose estimation methods have made significant progress but give less consideration to the inner interactions of input data, especially when consuming hand point clouds. Therefore, this paper proposes an end-to-end capsule-based hand pose estimation network (Capsule-HandNet), which processes hand point clouds directly with the consideration of structural relationships among local parts, including symmetry, junction, relative location, etc. Firstly, an encoder is adopted in Capsule-HandNet to extract multi-level features into the latent capsule by dynamic routing. The latent capsule represents the structural relationship information of the hand point cloud explicitly. Then, a decoder recovers a point cloud to fit the input hand point cloud via a latent capsule. This auto-encoder procedure is designed to ensure the effectiveness of the latent capsule. Finally, the hand pose is regressed from the combined feature, which consists of the global feature and the latent capsule. The Capsule-HandNet is evaluated on public hand pose datasets under the metrics of the mean error and the fraction of frames. The mean joint errors of Capsule-HandNet on MSRA and ICVL datasets reach 8.85 mm and 7.49 mm, respectively, and Capsule-HandNet outperforms the state-of-the-art methods on most thresholds under the fraction of frames metric. The experimental results demonstrate the effectiveness of Capsule-HandNet for 3D hand pose estimation.

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“…In particular, the cost of manually processing massive data is prohibitive. Excellent performance has been achieved on sentimental classification [13][14][15] and spam categorization [16,17], which belong to the field of text classification.…”

Section: Related Workmentioning

confidence: 99%

Attention Pooling-Based Bidirectional Gated Recurrent Units Model for Sentimental Classification

Zhang¹,

Hong²,

Han³

et al. 2019

IJCIS

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Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-term dependencies. Many models have achieved excellent results based on RNN. However, most of these models overlook the locations of the keywords in a sentence and the semantic connections in different directions. As a consequence, these methods do not make full use of the available information. Considering that different words in a sequence usually have different importance, in this paper, we propose bidirectional gated recurrent units (BGRUs) which integrates a novel attention pooling mechanism with max-pooling operation to force the model to pay attention to the keywords in a sentence and maintain the most meaningful information of the text automatically. The presented model allows to encode longer sequences. Thus, it not only prevents important information from being discarded but also can be used to filter noises. To avoid full exposure of content without any control, we add an output gate to the GRU, which is named as text unit. The proposed model was evaluated on multiple tasks, including sentimental classification, movie review data, and a subjective classification dataset. Experimental results show that our model can achieve excellent performance on these tasks.

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An efficient approach to directly compute the exact Hausdorff distance for 3D point sets

Cited by 61 publications

References 78 publications

Collaborative CAD Synchronization Based on a Symmetric and Consistent Modeling Procedure

Collaborative CAD Synchronization Based on a Symmetric and Consistent Modeling Procedure

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Attention Pooling-Based Bidirectional Gated Recurrent Units Model for Sentimental Classification

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