Geotextures: A Multi-source Geodesic Distance Field Approach for Procedural Texturing of Complex Meshes

Author(s):  
G N Oliveira ◽  
R P Torchelsen ◽  
J L D Comba ◽  
M Walter ◽  
R Bastos
2013 ◽  
Author(s):  
Karthik Krishnan

The computation of geodesic distances on a triangle mesh has many applications in geometry processing. The fast marching method provides an approximation of the true geodesic distance field. We provide VTK classes to compute geodesics on triangulated surface meshes. This includes classes for computing the geodesic distance field from a set of seeds and to compute the geodesic curve between source and destination point(s) by back-tracking along the gradient of the distance field. The fast marching toolkit (Peyre et. al.) is internally used. A variety of options are exposed to guide front propagation including the ability to specify propagation weights, constrain to a region, specify exclusion regions, and distance based termination criteria. Interpolators that plug into a contour widget, are provided to enable interactive tracing of paths on meshes.


2020 ◽  
Vol 127 ◽  
pp. 102879 ◽  
Author(s):  
Luming Cao ◽  
Junhao Zhao ◽  
Jian Xu ◽  
Shuangmin Chen ◽  
Guozhu Liu ◽  
...  

Author(s):  
Dong He ◽  
Yamin Li ◽  
Zhaoyu Li ◽  
Kai Tang

Abstract A critical task in multi-pass process planning for five-axis machining of complicated parts is to determine the intermediate surfaces for rough machining. Traditionally, the intermediate surfaces are simply parallel Z-level planes, and the machining is of the simplest three-axis type. However, for complicated parts, this so-called Z-level method lacks flexibility and causes isolated islands on layers, which require extraneous air movements by the tool. Moreover, the in-process workpiece machined according to the Z-level method suffers from the staircase effect, which often induces unstable dynamic problems on the tool-spindle system. In this paper, we propose a new method of planning a five-axis machining process for a complicated freeform solid part. In our method, the intermediate surfaces are no longer planar but curved, and they are intrinsically influenced by the convex hull of the part. The powerful algebraic tool of geodesic distance field is utilized to generate the desired intermediate surfaces, for which collision-free five-axis machining tool paths are then planned. In addition, we propose a novel idea of alternating between the roughing and finishing machining operations, which helps improve the stiffness of the in-process workpiece. Ample physical cutting experiments are performed, and the experimental results convincingly confirm the advantages of our method.


Author(s):  
S. Andrietti ◽  
M. Bernacki ◽  
N. Bozzolo ◽  
L. Maire ◽  
P. De Micheli ◽  
...  
Keyword(s):  

2010 ◽  
Vol 30 (2) ◽  
pp. 362-363
Author(s):  
Sheng CHEN ◽  
Xun LIU

Author(s):  
Sara Moccia ◽  
Maria Chiara Fiorentino ◽  
Emanuele Frontoni

Abstract Background and objectives Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R$$^{2}$$ 2 CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods Mask-R$$^{2}$$ 2 CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results Mask-R$$^{2}$$ 2 CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R$$^{2}$$ 2 CNN achieved a mean absolute difference of 1.95 mm (standard deviation $$=\pm 1.92$$ = ± 1.92  mm), outperforming other approaches in the literature. Conclusions With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R$$^{2}$$ 2 CNN may be an effective support for clinicians for assessing fetal growth.


2021 ◽  
pp. 168526
Author(s):  
Martin Puschmann ◽  
João C. Getelina ◽  
José A. Hoyos ◽  
Thomas Vojta

Author(s):  
M. Jeffery ◽  
J. Huang ◽  
S. Fityus ◽  
A. Giacomini ◽  
O. Buzzi

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