Academic literature on the topic 'Signed Distance Functions'
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Journal articles on the topic "Signed Distance Functions"
Bálint, Csaba, Gábor Valasek, and Lajos Gergó. "Operations on Signed Distance Functions." Acta Cybernetica 24, no. 1 (May 21, 2019): 17–28. http://dx.doi.org/10.14232/actacyb.24.1.2019.3.
Full textLuo, Honglin, Xianfu Wang, and Brett Lukens. "Variational Analysis on the Signed Distance Functions." Journal of Optimization Theory and Applications 180, no. 3 (October 26, 2018): 751–74. http://dx.doi.org/10.1007/s10957-018-1414-2.
Full textEsedog¯lu, Selim, Steven Ruuth, and Richard Tsai. "Diffusion generated motion using signed distance functions." Journal of Computational Physics 229, no. 4 (February 2010): 1017–42. http://dx.doi.org/10.1016/j.jcp.2009.10.002.
Full textFayolle, Pierre-Alain, Alexander Pasko, Benjamin Schmitt, and Nikolay Mirenkov. "Constructive Heterogeneous Object Modeling Using Signed Approximate Real Distance Functions." Journal of Computing and Information Science in Engineering 6, no. 3 (November 25, 2005): 221–29. http://dx.doi.org/10.1115/1.2218366.
Full textZollhöfer, Michael, Angela Dai, Matthias Innmann, Chenglei Wu, Marc Stamminger, Christian Theobalt, and Matthias Nießner. "Shading-based refinement on volumetric signed distance functions." ACM Transactions on Graphics 34, no. 4 (July 27, 2015): 1–14. http://dx.doi.org/10.1145/2766887.
Full textKoch, Philipp, Stefan May, Michael Schmidpeter, Markus Kühn, Christian Pfitzner, Christian Merkl, Rainer Koch, et al. "Multi-Robot Localization and Mapping Based on Signed Distance Functions." Journal of Intelligent & Robotic Systems 83, no. 3-4 (June 27, 2016): 409–28. http://dx.doi.org/10.1007/s10846-016-0375-7.
Full textDeutsch, Clayton V., and Brandon J. Wilde. "Modeling multiple coal seams using signed distance functions and global kriging." International Journal of Coal Geology 112 (June 2013): 87–93. http://dx.doi.org/10.1016/j.coal.2012.11.013.
Full textTao, Songqiao, and Juan Tan. "Path Planning with Obstacle Avoidance Based on Normalized R-Functions." Journal of Robotics 2018 (October 4, 2018): 1–10. http://dx.doi.org/10.1155/2018/5868915.
Full textKraft, Daniel. "Computing the Hausdorff Distance of Two Sets from Their Distance Functions." International Journal of Computational Geometry & Applications 30, no. 01 (March 2020): 19–49. http://dx.doi.org/10.1142/s0218195920500028.
Full textMolchanov, Vladimir, Paul Rosenthal, and Lars Linsen. "Non-iterative Second-order Approximation of Signed Distance Functions for Any Isosurface Representation." Computer Graphics Forum 29, no. 3 (August 12, 2010): 1211–20. http://dx.doi.org/10.1111/j.1467-8659.2009.01699.x.
Full textDissertations / Theses on the topic "Signed Distance Functions"
Laney, Daniel Edward. "Surface visualization and compression with signed-distance functions /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.
Full textHansson, Söderlund Herman. "Hardware-Accelerated Ray Tracing of Implicit Surfaces : A study of real-time editing and rendering of implicit surfaces." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21764.
Full textBakgrund. Triangelrastrering har varit den dominerande renderingstekniken inom realtidsgrafik i flera år. Trianglar är dock inte alltid lätta att jobba med för skapare av grafiska modeller. Med introduktionen av hårdvaruaccelererad strålspårning har rastreringsbaserade ljussättningstekniker stadigt ersatts av strålspårningstekniker. Detta skifte innebär att det kan finnas möjlighet för att utforska andra, mer lättredigerade geometrityper jämfört med triangelgeometri, exempelvis implicita ytor. Syfte. Detta examensarbete undersöker rendering- och redigeringshastigheten, samt bildkvaliteten av olika renderingstekniker för implicita ytor tillsammans med en spjutspetsalgoritm för hårdvaruaccelererad strålföljning. Den undersöker även hur implicita ytor kan redigeras i realtid och hur det påverkar rendering. Metod. En direkt sfärspårningsalgoritm implementeras som baslinje för att rendera implicita ytor. Även algoritmer som utför sfärstrålning över en kompakt- och smalbandsdiskretisering av den implicita ytan implementeras. För varje teknik implementeras även två variationer som potentiellt kan ge bättre prestanda. Utöver dessa renderingstekniker skapas även ett redigeringsverktyg för implicita ytor. Renderingshastighet, redigeringshastighet, och bildkvalité mäts för alla tekniker över flera olika scener som har skapats med redigeringsverktyget tillsammans med en hårdvaruaccelererad strålföljningsalgoritm. Skillnader i bildkvalité utvärderas med hjälp av mean squared error och evalueringsverktyget för bildskillnader som heter FLIP. Resultat. Direkt sfärspårning åstadkommer bäst bildkvalité, men har den långsammaste renderingshastigheten. Kompakt diskretisering renderar snabbast i de flesta tester och åstadkommer bättre bildkvalité än vad smalbandsdiskretisering gör. Smalbandsdiskretisering åstadkommer betydligt bättre redigeringshastighet än både direkt sfärspårning och kompakt diskretisering. Variationerna för respektive algoritm presterar alla lika bra eller bättre än standardvarianten för respektive algoritm. Alla algoritmer uppnår realtidsprestanda inom rendering och redigering. Endast diskretiseringsmetoderna uppnår dock realtidsprestanda för rendering med alla scener och endast smalbandsdiskretisering uppnår realtidsprestanda för redigering med ett större antal primitiver. Slutsatser. Implicita ytor kan renderas och redigeras i realtid tillsammans med en spjutspetsalgoritm för hårdvaruaccelererad strålföljning. Vid användning av direkt sfärstrålning minskar renderingshastigheten när den ytan består av ett stort antal primitiver. Diskretiseringstekniker har dock en renderingshastighet som är oberoende av antalet primitiver. Smalbandsdiskretisering är tillräckligt snabb för att redigering ska kunna ske i realtid även för implicita ytor som består stora antal primitiver.
Bengtsson, Morgan. "Indoor 3D Mapping using Kinect." Thesis, Linköpings universitet, Datorseende, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-106145.
Full textUnder de senaste åren har flera olika avståndskameror lanserats på konsumentmarkanden. Detta har skapat många intressanta applikationer både i professionella system samt för underhållningssyfte. Ett exempel på en sådan kamera är Microsoft Kinect som utvecklades för Microsofts spelkonsol Xbox 360. I detta examensarbete presenteras ett system som använder Kinect för att skapa en så exakt rekonstruktion i 3D av en innomhusmiljö som möjligt. Den främsta innovationen i systemet är en datastruktur baserad på signed distance fields (SDF) och octrees, vilket används för att representera den rekonstruerade miljön.
Rundgren, Emil. "Automatic Volume Estimation of Timber from Multi-View Stereo 3D Reconstruction." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142513.
Full textSouza, Ricardo Radtke de. "Modelagem geológica implícita através de simulações de funções distância assinaladas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/172276.
Full textBefore making an estimation or a geostatistical simulation, geological domains must be modeled so that each domain uses only data that belongs to it. In the mineral industry the uncertainty derived from the grades is generally taken into account, however the uncertainty generated by the model is not always analyzed. Knowing that the greatest source of uncertainty is in the transition from one lithology to another, this dissertation aims to evaluate the uncertainty of the geological model through signed distances function simulation in uncertainty zones, generating several models with different proportions of each lithology. A case study in a real dataset with high geological complexity is used to evaluate the use of the methodology. The method proved effective in assessing the impact of the volume difference that each lithology can reach, demonstrating the importance of measuring uncertainty in the construction of geological models.
Rolo, Roberto Mentzingen. "Modelagem geológica implícita com funções distância assinaladas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163406.
Full textPrior to every geostatistical estimation or simulation study there is a need for delimiting the geologic domains of the deposit, which is traditionally done manually by a geomodeler in a laborious, time consuming and subjective process. For this reason, novel techniques referred to as implicit modelling have appeared. These techniques provide algorithms that replace the manual digitization process of the traditional methods by some form of automatic procedure. This dissertation covers a few well established implicit methods currently available with special attention to the signed distance function methodology. A case study based on a real dataset was performed and its applicability discussed. Although it did not replace an experienced geomodeler, the method proved to be capable in creating semi-automatic geological models from the sampling data, especially in the early stages of exploration.
Su, Po-Chang. "A ROBUST RGB-D SLAM SYSTEM FOR 3D ENVIRONMENT WITH PLANAR SURFACES." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/17.
Full textMichailidis, Georgios. "Manufacturing Constraints and Multi-Phase Shape and Topology Optimization via a Level-Set Method." Phd thesis, Ecole Polytechnique X, 2014. http://pastel.archives-ouvertes.fr/pastel-00937306.
Full textBartoli, Simone. "Deploying deep learning for 3D reconstruction from monocular video sequences." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22402/.
Full textHunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.
Full textBooks on the topic "Signed Distance Functions"
Mann, Peter. Matrices. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0031.
Full textBook chapters on the topic "Signed Distance Functions"
Osher, Stanley, and Ronald Fedkiw. "Signed Distance Functions." In Applied Mathematical Sciences, 17–22. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-22746-6_2.
Full textOsher, Stanley, and Ronald Fedkiw. "Constructing Signed Distance Functions." In Applied Mathematical Sciences, 63–74. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-22746-6_7.
Full textCavallari, Tommaso, and Luigi Di Stefano. "Volume-Based Semantic Labeling with Signed Distance Functions." In Image and Video Technology, 544–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29451-3_43.
Full textMahmoodi, Sasan, Muayed S. Al-Huseiny, and Mark S. Nixon. "Similarity Registration for Shapes Based on Signed Distance Functions." In Advances in Visual Computing, 599–609. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_57.
Full textWerner, Diana, Ayoub Al-Hamadi, and Philipp Werner. "Truncated Signed Distance Function: Experiments on Voxel Size." In Lecture Notes in Computer Science, 357–64. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11755-3_40.
Full textWójcik, Krzysztof, Bogdan Wziętek, Piotr Wziętek, and Marcin Piekarczyk. "Signal Recognition Based on Multidimensional Optimization of Distance Function in Medical Applications." In Computer Networks, 410–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39207-3_35.
Full textKim, Jung Hwan, Amanda Taylor, and David Ress. "Simple Signed-Distance Function Depth Calculation Applied to Measurement of the fMRI BOLD Hemodynamic Response Function in Human Visual Cortex." In Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications, 216–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54609-4_16.
Full textDong, Wei, Qiuyuan Wang, Xin Wang, and Hongbin Zha. "PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction." In Computer Vision – ECCV 2018, 714–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_43.
Full textLin, Guishan, Zhiming He, Jing Wu, Meng Zhao, and Zhihao Mei. "Wideband Radar Target Detection Based on the Probability Distance of Empirical Cumulative Distribution Function." In The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems, 617–23. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00536-2_71.
Full textJaggi, Chandra K., Anuj Sharma, and Reena Jain. "EOQ Model with Permissible Delay in Payments under Fuzzy Environment." In Advances in Business Information Systems and Analytics, 281–96. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5958-2.ch014.
Full textConference papers on the topic "Signed Distance Functions"
Haugo, Simen, Annette Stahl, and Edmund Brekke. "Continuous Signed Distance Functions for 3D Vision." In 2017 International Conference on 3D Vision (3DV). IEEE, 2017. http://dx.doi.org/10.1109/3dv.2017.00023.
Full textGong, Yuanhao, Gregory Paul, and Ivo F. Sbalzarini. "Coupled signed-distance functions for implicit surface reconstruction." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235726.
Full textYan, Jun, Qing Xia, YiJun Ji, and ZhiJiang Zhang. "Localization of transparent objects using signed distance functions." In Sixth International Conference on Optical and Photonic Engineering, edited by Yingjie Yu, Chao Zuo, and Kemao Qian. SPIE, 2018. http://dx.doi.org/10.1117/12.2326963.
Full textBrissman, Emil, Joakim Johnander, and Michael Felsberg. "Predicting Signed Distance Functions for Visual Instance Segmentation." In 2021 Swedish Artificial Intelligence Society Workshop (SAIS). IEEE, 2021. http://dx.doi.org/10.1109/sais53221.2021.9484039.
Full textPark, Jeong Joon, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00025.
Full textDietrich, Vincent, Dong Chen, Kai M. Wurm, Georg v. Wichert, and Philipp Ennen. "Probabilistic multi-sensor fusion based on signed distance functions." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487333.
Full textSchmidt, Nicolas. "A CAD Interface for Drawing with Signed Distance Functions." In SIGGRAPH '20: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388770.3407435.
Full textKoch, Philipp, Stefan May, Michael Schmidpeter, Markus Kuhn, Christian Pfitzner, Christian Merkl, Rainer Koch, Martin Fees, Jon Martin, and Andreas Nuchter. "Multi-robot Localization and Mapping Based on Signed Distance Functions." In 2015 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2015. http://dx.doi.org/10.1109/icarsc.2015.18.
Full textDaun, Kevin, Stefan Kohlbrecher, Jurgen Sturm, and Oskar von Stryk. "Large Scale 2D Laser SLAM using Truncated Signed Distance Functions." In 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2019. http://dx.doi.org/10.1109/ssrr.2019.8848964.
Full textMorgan, John P., and Richard L. Tutwiler. "Real-time reconstruction of depth sequences using signed distance functions." In SPIE Defense + Security, edited by Ivan Kadar. SPIE, 2014. http://dx.doi.org/10.1117/12.2054158.
Full textReports on the topic "Signed Distance Functions"
Kuznetsov, Victor, Vladislav Litvinenko, Egor Bykov, and Vadim Lukin. A program for determining the area of the object entering the IR sensor grid, as well as determining the dynamic characteristics. Science and Innovation Center Publishing House, April 2021. http://dx.doi.org/10.12731/bykov.0415.15042021.
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