Quantitative optimization of interoperability during feature-based data exchange

Zhang, Dejun; He, Fazhi; Han, Soonhung; Li, Xiaoxia

doi:10.3233/ica-150499

Cited by 87 publications

(13 citation statements)

References 72 publications

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“…(1) The qualitative evaluation of model similarity, such as similarity assessments for 3D CAD/ CAE model retrieval [10,17], focused on the qualitative aspects of the models, e.g., topological result and geometric profile. On the other hand, in the field of data exchange [29,37,42,51,57,58,67,69] and collaborative design [19,20,33,44,45,49], the quantitative comparison of the similarity between source and target of feature-based CAD models is the latest development [72]. So the proposed method can be adopted in this area to improve the computing efficiency of quantitative comparisons between large scale models in engineering applications of CAD/CAE/CAM in the future.…”

Section: Discussionmentioning

confidence: 99%

“…So the proposed method can be adopted in this area to improve the computing efficiency of quantitative comparisons between large scale models in engineering applications of CAD/CAE/CAM in the future. For example, the proposed algorithms can enhance the computing efficiency of quantitative comparisons in Feature-based Data Exchange in CAD/CAE/CAM when calculating the fitness in optimization computation [72]. (2) This work is also related with design automation because the exact Hausdorff distance will be automatically computed.…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Han

et al. 2017

ICA

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Hausdorff distance measure is very important in CAD/CAE/CAM related applications. This manuscript presents an efficient framework and two complementary subalgorithms to directly compute the exact Hausdorff distance for general 3D point sets. The first algorithm of Nonoverlap Hausdorff Distance (NOHD) combines branch-and-bound with early breaking to cut down the Octree traversal time in case of spatial nonoverlap. The second algorithm of Overlap Hausdorff Distance (OHD) integrates a point culling strategy and nearest neighbor search to reduce the number of points traversed in case of spatial overlap. The two complementary subalgorithms can achieve a highly efficient and balanced result. Both NOHD and OHD compute the exact Hausdorff distance directly for arbitrary 3D point sets. We conduct a number of experiments on benchmark models and CAD application models, and compare the proposed approach with other state-of-the-art algorithms. The results demonstrate the effectiveness of our method.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Zhang

Han

et al. 2017

ICA

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“…In future work, we will explore new models of HW/SW co-design and new acceleration methods on Multi-core SIMD CPUs (Ouyang, et al, 2016), (Zhou, et al, 2017). We will also try to extend the proposed idea and method to other areas, such as large-scale data visualization and rendering (Chen, et al, 2017), (Inui M, et al, 2016), (Kim, Kyung and Lee, 2012), (Mandachi, Usuki and Miura, 2014), (Umezu, 2013), (Zhang, et al, 2017), large-scale co-operation editing in text and geometry in Computer-Supproted Cooperation Work (Cheng, et al, 2016), (Lv, et al, 2016), (Nomaguchi,Tsutsumi and Fujita,212), large-scale 3D model interchange and retrieval in CAD (Wu, et al, 2016), (Zhang, et al, 2016), (Komoto, Kondoh and Masui, 2016), (Yeoun and Kim, 2016), (Qin, et al, 2016, (Qin, et al, 2016 , and real-time video and large HD image processing in computer vision (Li, He and Chen, 2016), (Ni, et al, 2016), (Li, He and Chen, 2017), (Li, et al, 2017), (Li, et al, 2017), (Sun, et al, 2016), (Liu, et al, 2016),…”

Section: Discussionmentioning

confidence: 99%

A GPU-based tabu search for very large hardware/software partitioning with limited resource usage

Hou

Zhou

et al. 2017

JAMDSM

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In hardware/software (HW/SW) co-design, HW/SW partitioning is the most important step since it determines which components are implemented in hardware and which are implemented in software. Since most of HW/SW partitioning problems are NP hard, heuristic methods have to be utilized to solve them, especially for the large size problems. GPU-based heuristic methods to accelerate HW/SW co-design are a promising way to reduce run time. However, the existing methods cannot deal with very large embedded applications because of GPU resource limitations. This paper presents a method to overcome the GPU resource limitations for very large partitioning while keeping a reasonable runtime. First, at the stage of computing the costs of the candidates, we propose a fast method of 2-flipping computing for very large HW/SW co-design. Our method is also general and can deal with both odd and even numbers of nodes. More importantly, our method avoids utilizing doubleprecision arithmetic units, which are scarce resources in GPU architecture. Second, since the GPU is constrained by memory limitations and the costs of candidates cannot be directly stored in the GPU's global memory, we present a time-space tradeoff strategy to break memory limitations for very large HW/SW partitioning. In this way, the following steps can be run under the constraint of GPU's memory limitations. Third, an in-place removal of infeasible solutions is proposed to reduce the overhead of global memory by half when the neighborhood is compacted. Fourth, when evaluating the tabu status of feasible candidates, we present a bitwise representation of tabu status to minimize the transfer overhead. Finally, we conduct a number of experiments. The results show that the proposed 2-flipping method of single precision data types works well. The results also demonstrate that the proposed approach expands the number of nodes of the task graph from 10,000 to 30,000 under the limitation of the GPU's global memory of 6 GB. The correlations between compression intensity and solution quality are analyzed to ensure the fairness and soundness of our method. Our work is general and can provide guidance for other applications.

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“…The advance of RGB-D images brings about a new area of research around using the 3D information in image processing and computer vision and the opportunity to some challenging problems like object recognition [BO 2013] [LAI 2011], understanding [Silberman 2012] [Gupta 2013] [Zhang 2013] and reconstruction [Izadi 2011] [Henry 2010] [Henry 2012] of indoor scenes. Modeling and reconstruction is also a basic problem in CAD/CAM, Mechanical design, and Architectural Design [Zhang 2016] [Cai 2015] [Li 2013] [Cheng 2013]. In this paper, we choose to parse an indoor scene, focus on extracting the walls, floor and ceiling from a depth image since these represent the main structures of the indoor scene and provide a basis on inferring other structures such as doors, windows, tables and beds.…”

Section: Motivationmentioning

confidence: 99%

Parsing main structures of indoor scenes from single RGB-D image

Zhou

Pan

et al. 2016

JAMDSM

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Parsing indoor scenes from RGB-D imagery is a significant and challenging task in image understanding and computer vision. Since the walls, floor and ceiling are the main structures of indoor scenes, we propose a simple and efficient method to find them from a single depth image. We use the prior knowledge of the walls, floor and ceiling and distance histogram to obtain their planes, and delimit their extents by ray-casting. Our method can also detect the open windows or doors and other planes which have large area in indoor scenes. Experimental results show that our method is more robust to find the main structures of the indoor scenes than the existing method.

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Quantitative optimization of interoperability during feature-based data exchange

Cited by 87 publications

References 72 publications

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

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

A GPU-based tabu search for very large hardware/software partitioning with limited resource usage

Parsing main structures of indoor scenes from single RGB-D image

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