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A new vector field distance transform and its application to mesh processing from 3D scanned data
2007
The Visual Computer
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In this paper we define a new 3D vector field distance transform to implicitly represent a mesh surface. ...
Widely used Marching Cube triangulation algorithm is adapted to the new vector field distance transform to correctly reconstruct the resulting explicit surface. ...
Acknowledgment We wish to thank the following sources for the use of the models included in this paper: ...
doi:10.1007/s00371-007-0143-6
fatcat:gtsjusdztfcujjddxlmaoi7rwa
Generation of micro-scale finite element models from synchrotron X-ray CT images for multidirectional carbon fibre reinforced composites
2016
Composites. Part A, Applied science and manufacturing
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The new algorithm uses a global overlapping stack filtering step followed by a local fibre tracking step. Both steps are based on the Bayesian inference theory. ...
are then used to generate micro-scale finite element models. ...
These were implemented by an inverse exponential fitting to find the adaptive threshold coefficients using Fig. 10 . ...
doi:10.1016/j.compositesa.2016.09.010
fatcat:pjgaulyfpnalhagcgv3y7423nm
Investigation of Smoothness-Increasing Accuracy-Conserving Filters for Improving Streamline Integration through Discontinuous Fields
2008
IEEE Transactions on Visualization and Computer Graphics
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We investigate whether such an approach applied to uniform quadrilateral discontinuous Galerkin (high-order finite volume) data can be used to augment current adaptive error control approaches. ...
As the root of the difficulties is the discontinuous nature of the data, we present a complementary approach of applying smoothness-increasing accuracy-conserving filters to the data prior to streamline ...
ACKNOWLEDGMENTS The first author would like to acknowledge support from the US Department of Energy through the Center for the Simulation of Accidental Fires and Explosions (C-SAFE) under Grant W-7405- ...
doi:10.1109/tvcg.2008.9
pmid:18369273
fatcat:dz4b5o4p6nhijcz7soggmqleue
Cross-Atlas Convolution for Parameterization Invariant Learning on Textured Mesh Surface
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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We present a convolutional network architecture for direct feature learning on mesh surfaces through their atlases of texture maps. ...
Since the parameterization of texture map is not pre-determined, and depends on the surface topologies, we therefore introduce a novel cross-atlas convolution to recover the original mesh geodesic neighborhood ...
Receptive fields Although the convolution is applied over 2D texture maps, its receptive field follows the mesh geodesic distance. This is done by using the offset map to redirect pixel locations. ...
doi:10.1109/cvpr.2019.00630
dblp:conf/cvpr/LiLZ0SFQ19
fatcat:gx7cj37d6zdcbbq3igyyonfa7a
Feature-Preserving Volume Filtering
[article]
2002
EUROVIS 2005: Eurographics / IEEE VGTC Symposium on Visualization
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Previous global 3D methods are restricted to binary volumes or segmented iso-surfaces and they are based on area minimization of one single reconstructed surface. ...
Although the strength of the presented algorithm is demonstrated on a specific 2D and a specific 3D application, it is considered as a general mathematical tool for processing images and volumes. ...
The 2D and 3D convolution kernels ...
doi:10.2312/vissym/vissym02/105-114
fatcat:glbfo7a3x5gptp3tozv77jwxzu
From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs
2016
IEEE Robotics and Automation Letters
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A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. ...
We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF ...
Generating the volumetric filter kernels from the 1D coefficient vectors is analogous to the 2D case and is detailed in Algorithm 1. ...
doi:10.1109/lra.2016.2523555
dblp:journals/ral/CanelhasSL16
fatcat:owilo4lfmfa65lsusyeultj2ay
Lines of Curvature for Polyp Detection in Virtual Colonoscopy
2006
IEEE Transactions on Visualization and Computer Graphics
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We have adapted existing approaches on explicit triangular surface meshes, and developed a new algorithm on implicit surfaces embedded in 3D volume data. ...
Most of the previous work on automatic polyp detection makes use of indicators based on the scalar curvature of the colon wall and can result in many false-positive detections. ...
Frans A. Gerritsen and Ir. C. van Wijk for kind advices and helpful discussions, as well as providing the authors with CT scans of patients. ...
doi:10.1109/tvcg.2006.158
pmid:17080813
fatcat:wpjwcsf6qjcythw3dv3w5no5lu
A Non-linear Differential CNN-Rendering Module for 3D Data Enhancement
[article]
2019
arXiv
pre-print
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Our module is learnable and can be easily integrated into neural networks allowing to optimize data rendering towards specific learning tasks using gradient based methods in an end-to-end fashion. ...
In our experiments, we apply our module to demonstrate efficient localization and classification tasks in cluttered data both 2D and 3D. ...
Similar to us, ProbNet [23] use a sparse set of probes in 3D space and a volume distance field the sense shape geometry features. ...
arXiv:1904.04850v1
fatcat:4neyi6oh7jhzzjvfmeboxotucm
VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes
[article]
2018
arXiv
pre-print
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Voxel is an important format to represent geometric data, which has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. ...
In this paper, we propose a novel volumetric convolutional neural network, which could extract discriminative features encoding detailed information from voxelized 3D data under a limited resolution. ...
[29] presented a learning-based method to segment and label 3D meshes by combining various geometric features. Later, Guo et al. ...
arXiv:1809.00226v1
fatcat:rlxoahgzdvdcbbdmv3j453tp2q
A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications
2020
IEEE Access
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Therefore, it can be concluded that deep learning together with a suitable 3D data representation gives an effective approach for improving the performance of 3D shape analysis. ...
However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. ...
In [4] , a voxel-based method was used to characterize a 3D shape and use a 3D CNN to the whole volume. References [79] , [80] used the features describe on a manifold to execute CNN operations. ...
doi:10.1109/access.2020.2982196
fatcat:jnya5rscynf3zm7efuucqxafri
Segmentation of distal airways using structural analysis
2019
PLoS ONE
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We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired ...
Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. ...
Acknowledgments Dedicated to "la banquera solidaria, mi Heidi alcoholica y los suculentos biberones veraniegos". Visualization: Agnes Borras.
Writing -original draft: Debora Gil. ...
doi:10.1371/journal.pone.0226006
pmid:31856216
pmcid:PMC6922352
fatcat:u2k22pv6hzfyngxxtni65ay3fe
Analytical solutions for sketch-based convolution surface modeling on the GPU
2011
The Visual Computer
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For finite support kernel functions, a skeleton clipping algorithm is presented to compute the valid skeletons. ...
We convert the double integral over a planar polygon into a simple integral along the contour of the polygon based on Green's theorem, which reduces the computational cost and allows for efficient parallel ...
A convolution surface is defined by convolving a skeleton with a three-dimensional low-pass filter kernel. The skeletal elements can be points, polylines, polygons, and even volumes. ...
doi:10.1007/s00371-011-0662-z
fatcat:2pflrrli5zfd5mxah2bwkxfx6m
Real-time Rendering of Stack-based Terrains
[article]
2011
International Symposium on Vision, Modeling, and Visualization
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In contrast, a 3D data representation quickly exceeds the available memory resources. To overcome this problem we apply material stacks. ...
Usually, terrain rendering relies on a 2D regular grid of height values, the so called height field. Height fields describe 2.5D surfaces and are not able to present complex 3D terrain features. ...
For 3D terrain data similar data-structures can be applied. For instance, in [Gei07, GT07] an octree is used to manage 3D terrain volume. ...
doi:10.2312/pe/vmv/vmv11/161-168
dblp:conf/vmv/LofflerMS11
fatcat:6u6a4gvapnfjbjwbybspvbquwy
3D Model Generation and Reconstruction Using Conditional Generative Adversarial Network
2019
International Journal of Computational Intelligence Systems
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Moreover, to better guide generator to reconstruct 3D model from a single image in high quality, we propose a new 3D model reconstruction network by integrating a classifier into the traditional system ...
To address this problem, we add the class information to both generator and discriminator and construct a new network named 3D conditional GAN. ...
a Single Image If a 3D model of a specified condition can be generated from a random vector, it's possible to generate a 3D model corresponding to an image by encoding the image and using encoded result ...
doi:10.2991/ijcis.d.190617.001
fatcat:mkkkbwi7sbbwjeukvinbrwplwy
Learning Depth-Guided Convolutions for Monocular 3D Object Detection
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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To better represent 3D structure, prior arts typically transform depth maps estimated from 2D images into a pseudo-LiDAR representation, and then apply existing 3D point-cloud based object detectors. ...
LCN (D 4 LCN), where the filters and their receptive fields can be automatically learned from image-based depth maps, making different pixels of different images have different filters. ...
Since there are huge intra-class and inter-class scale differences in an RGB image, we use I to learn an adaptive dilation rate for each filter to obtain different sizes of receptive fields by an adaptive ...
doi:10.1109/cvpr42600.2020.01169
dblp:conf/cvpr/DingHYWSLL20a
fatcat:uol5jq2n25gwjdzghz7eke63i4
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