No edit summary |
No edit summary |
||
| Line 3: | Line 3: | ||
==Literature Review== | ==Literature Review== | ||
=== | [[User:Aliaksei Petsiuk|Aliaksei Petsiuk]] ([[User talk:Aliaksei Petsiuk|talk]]) 14:32, 20 May 2019 (PDT) | ||
===MOST Papers=== | |||
1. ''Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M.'' '''[https://www.mdpi.com/2504-4494/1/1/2 Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views.]''' J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002. <ref>Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002.</ref> | 1. ''Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M.'' '''[https://www.mdpi.com/2504-4494/1/1/2 Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views.]''' J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002. <ref>Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002.</ref> | ||
| Line 15: | Line 17: | ||
*--- | *--- | ||
--- | ---- | ||
===2019=== | |||
5. [https://dashcamtalk.com/cams/lk-7950-wd/Sony_IMX322.pdf '''SONY IMX322 Datasheet'''] (accessed on 16 May 2019). <ref>SONY IMX322 Datasheet. SONY, 2019 (accessed on 16 May 2019).</ref> | |||
7. [http://marlinfw.org/ '''Marlin Open-Source RepRap Firmware'''] (accessed on 16 May 2019). <ref>Marlin Open-Source RepRap Firmware (accessed on 16 May 2019).</ref> | |||
---- | |||
===2018=== | |||
3. [http://wohlersassociates.com/2018report.htm '''Wohlers Report.'''] Annual worldwide progress report in 3D Printing, 2018. <ref>Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.</ref> | 3. [http://wohlersassociates.com/2018report.htm '''Wohlers Report.'''] Annual worldwide progress report in 3D Printing, 2018. <ref>Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.</ref> | ||
10. Ugandhar Delli, Shing Chang. Automated processes monitoring in 3D printing using supervised machine learning. Kansas State University, 2018. | |||
19. L. Scime, J. Beuth. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Carnegie Mellon University, Pittsburgh, PA, United States. Additive Manufacturing 19 (2018) 114–126. https://reader.elsevier.com/reader/sd/pii/S221486041730180X?token=8B3C9C53E15A52C70F6342A81E66549CF761625A88031F38586742AFF7F1058EDF1F4F9669489BFAAC11E34D560037CC | |||
---- | |||
===2017=== | |||
9. B. Wang, F. Zhong, X. Qin, Pose Optimization in Edge Distance Field for Textureless 3D Object Tracking, 2017. | |||
https://imbinwang.github.io/static/assets/pdf/landing/2017_CGI_a32_wang.pdf | |||
20. K. Garanger, E. Feron, P-L. Garoche, J. Rimoli, J. Berrigan, M. Grover, K. Hobbs. Foundations of Intelligent Additive Manufacturing. Georgia Institute of Technology (GA, USA), ONERA (France), Air Force Research Laboratory (OH, USA). May 2017. https://arxiv.org/pdf/1705.00960.pdf | |||
---- | |||
===2016=== | |||
4. ''F. Thewihsen, S. Karevska, A. Czok, C. Pateman-Jones, D. Krauss.'' [https://www.ey.com/Publication/vwLUAssets/ey-3d-printing-report/$FILE/ey-3d-printing-report.pdf '''EY’s Global 3D printing Report.'''] 2016. <ref>F. Thewihsen, S. Karevska, A. Czok, C. Pateman-Jones, D. Krauss. EY’s Global 3D printing Report, 2016.</ref> | |||
11. J. Fastowicz, K. Okarma. Texture based quality assessment of 3D prints for different lighting conditions. In Proceedings of the International Conference on Computer Vision and Graphics, ICCVG (2016), 17-28. | |||
---- | |||
===2015=== | |||
18. O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. University of Freiburg, Germany, 2015. | |||
---- | |||
===2014=== | |||
7. A. Crivellaro; V. Lepetit : Robust 3D Tracking with Descriptor Fields. 2014. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 23-28, 2014. | |||
---- | |||
===2012=== | |||
16. C. Choi, H. Christensen. 3D Textureless Object Detection and Tracking: An Edge-based Approach, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. | |||
https://smartech.gatech.edu/bitstream/handle/1853/46853/3D%20Textureless%20Object%20Detection%20and%20Tracking%20An%20Edge-based%20Approach.pdf?sequence=1&isAllowed=y] | |||
17. M.Oikawa, M.Fujisawa, Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects, May 2012. http://imd.naist.jp/imdweb/pub/oikawa_svr12/paper.pdf | |||
---- | |||
===2010-2000=== | |||
13. J. Barandiaran, D. Borro. Edge-based markerless 3D tracking og rigid objects, 17th International Conference on Artificial Reality and Telexistence 2007. | |||
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4414647 | |||
22. Wuest, Harald, Florent Vial, and D. Strieker. Adaptive line tracking with multiple hypotheses for augmented reality. Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’05). IEEE, 2005. | |||
26. K. Grauman and T. Darrell, “The pyramid match kernel: Discriminative classification with sets of image features,” in ICCV, vol. 2, 2005, pp. 1458–1465 Vol. 2. | |||
15. I. Gordon and D. Lowe. Scene Modelling, Recognition and Tracking with Invariant Image Features, Conference: Mixed and Augmented Reality, 2004. ISMAR 2004. | |||
https://www.cs.ubc.ca/~lowe/papers/gordon04.pdf | |||
25. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” IJCV, vol. 60, no. 2, pp. 91–110, 2004 | |||
6. ''R. Hartley, A. Zisserman.'' '''Multiple View Geometry in Computer Vision. Cambridge University Press,''' 2003. <ref>R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.</ref> | |||
14. T. Drummond, R. Cipolla. Real-Time Visual Tracking of Complex Structures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002. | |||
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1017620 | |||
---- | |||
===2000 - ...=== | |||
23. Michael Isard and Andrew Blake. 1998. CONDENSATION - Conditional Density Propagation for Visual Tracking. In International Journal of Computer Vision. 5–28. | |||
24. R. Storn, K. Price, Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, December 1997, Volume 11, Issue 4, pp 341–359. http://www1.icsi.berkeley.edu/~storn/TR-95-012.pdf | |||
12. M. Armstrong A. Zisserman. Robust Object Tracking, Asian Conference on Computer Vision, vol. 1, pp. 58-61, 1995. http://www.robots.ox.ac.uk:5000/~vgg/publications/1995/Armstrong95/armstrong95.pdf | |||
8. Chris Harris and Carl Stennet. 1990. RAPID – A video-Rate Object Tracker. British Machine Vision Conference. | |||
28. J. Canny, “A computational approach to edge detection,” PAMI, pp. 679–698, 1986. | |||
2. ''Pierre Alfred Leon Ciraud.'' '''[https://patents.google.com/patent/DE2263777A1/en A method and apparatus for manufacturing objects made of any arbitrary material meltable.]''' German patent application DE2263777A1. December 28, 1971. <ref>Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.</ref> | |||
The first patent in the field of additive manufacturing. | |||
---- | |||
21. M. Lowney, A.S.Raj. Model Based Tracking for Augmented Reality an Mobile Devices. Dept. of Electrical Engineering, Stanford university. | |||
==References== | ==References== | ||
Revision as of 21:32, 20 May 2019
Literature Review
Aliaksei Petsiuk (talk) 14:32, 20 May 2019 (PDT)
MOST Papers
1. Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002. [1]
Abstract Prosumer (producing consumer)-based desktop additive manufacturing has been enabled by the recent radical reduction in 3-D printer capital costs created by the open-source release of the self-replicating rapid prototype (RepRap). To continue this success, there have been some efforts to improve reliability, which are either too expensive or lacked automation. A promising method to improve reliability is to use computer vision, although the success rates are still too low for widespread use. To overcome these challenges an open source low-cost reliable real-time optimal monitoring platform for 3-D printing from double cameras is presented here. This error detection system is implemented with low-cost web cameras and covers 360 degrees around the printed object from three different perspectives. The algorithm is developed in Python and run on a Raspberry Pi3 mini-computer to reduce costs. For 3-D printing monitoring in three different perspectives, the systems are tested with four different 3-D object geometries for normal operation and failure modes. This system is tested with two different techniques in the image pre-processing step: SIFT and RANSAC rescale and rectification, and non-rescale and rectification. The error calculations were determined from the horizontal and vertical magnitude methods of 3-D reconstruction images. The non-rescale and rectification technique successfully detects the normal printing and failure state for all models with 100% accuracy, which is better than the single camera set up only. The computation time of the non-rescale and rectification technique is two times faster than the SIFT and RANSAC rescale and rectification technique.
Notes
- ---
- ---
- ---
2019
5. SONY IMX322 Datasheet (accessed on 16 May 2019). [2]
7. Marlin Open-Source RepRap Firmware (accessed on 16 May 2019). [3]
2018
3. Wohlers Report. Annual worldwide progress report in 3D Printing, 2018. [4]
10. Ugandhar Delli, Shing Chang. Automated processes monitoring in 3D printing using supervised machine learning. Kansas State University, 2018.
19. L. Scime, J. Beuth. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Carnegie Mellon University, Pittsburgh, PA, United States. Additive Manufacturing 19 (2018) 114–126. https://reader.elsevier.com/reader/sd/pii/S221486041730180X?token=8B3C9C53E15A52C70F6342A81E66549CF761625A88031F38586742AFF7F1058EDF1F4F9669489BFAAC11E34D560037CC
2017
9. B. Wang, F. Zhong, X. Qin, Pose Optimization in Edge Distance Field for Textureless 3D Object Tracking, 2017. https://imbinwang.github.io/static/assets/pdf/landing/2017_CGI_a32_wang.pdf
20. K. Garanger, E. Feron, P-L. Garoche, J. Rimoli, J. Berrigan, M. Grover, K. Hobbs. Foundations of Intelligent Additive Manufacturing. Georgia Institute of Technology (GA, USA), ONERA (France), Air Force Research Laboratory (OH, USA). May 2017. https://arxiv.org/pdf/1705.00960.pdf
2016
4. F. Thewihsen, S. Karevska, A. Czok, C. Pateman-Jones, D. Krauss. EY’s Global 3D printing Report. 2016. [5]
11. J. Fastowicz, K. Okarma. Texture based quality assessment of 3D prints for different lighting conditions. In Proceedings of the International Conference on Computer Vision and Graphics, ICCVG (2016), 17-28.
2015
18. O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. University of Freiburg, Germany, 2015.
2014
7. A. Crivellaro; V. Lepetit : Robust 3D Tracking with Descriptor Fields. 2014. Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 23-28, 2014.
2012
16. C. Choi, H. Christensen. 3D Textureless Object Detection and Tracking: An Edge-based Approach, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012.
https://smartech.gatech.edu/bitstream/handle/1853/46853/3D%20Textureless%20Object%20Detection%20and%20Tracking%20An%20Edge-based%20Approach.pdf?sequence=1&isAllowed=y]
17. M.Oikawa, M.Fujisawa, Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects, May 2012. http://imd.naist.jp/imdweb/pub/oikawa_svr12/paper.pdf
2010-2000
13. J. Barandiaran, D. Borro. Edge-based markerless 3D tracking og rigid objects, 17th International Conference on Artificial Reality and Telexistence 2007. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4414647
22. Wuest, Harald, Florent Vial, and D. Strieker. Adaptive line tracking with multiple hypotheses for augmented reality. Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’05). IEEE, 2005.
26. K. Grauman and T. Darrell, “The pyramid match kernel: Discriminative classification with sets of image features,” in ICCV, vol. 2, 2005, pp. 1458–1465 Vol. 2.
15. I. Gordon and D. Lowe. Scene Modelling, Recognition and Tracking with Invariant Image Features, Conference: Mixed and Augmented Reality, 2004. ISMAR 2004. https://www.cs.ubc.ca/~lowe/papers/gordon04.pdf
25. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” IJCV, vol. 60, no. 2, pp. 91–110, 2004
6. R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003. [6]
14. T. Drummond, R. Cipolla. Real-Time Visual Tracking of Complex Structures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1017620
2000 - ...
23. Michael Isard and Andrew Blake. 1998. CONDENSATION - Conditional Density Propagation for Visual Tracking. In International Journal of Computer Vision. 5–28.
24. R. Storn, K. Price, Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, December 1997, Volume 11, Issue 4, pp 341–359. http://www1.icsi.berkeley.edu/~storn/TR-95-012.pdf
12. M. Armstrong A. Zisserman. Robust Object Tracking, Asian Conference on Computer Vision, vol. 1, pp. 58-61, 1995. http://www.robots.ox.ac.uk:5000/~vgg/publications/1995/Armstrong95/armstrong95.pdf
8. Chris Harris and Carl Stennet. 1990. RAPID – A video-Rate Object Tracker. British Machine Vision Conference.
28. J. Canny, “A computational approach to edge detection,” PAMI, pp. 679–698, 1986.
2. Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971. [7]
The first patent in the field of additive manufacturing.
21. M. Lowney, A.S.Raj. Model Based Tracking for Augmented Reality an Mobile Devices. Dept. of Electrical Engineering, Stanford university.
References
- ↑ Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002.
- ↑ SONY IMX322 Datasheet. SONY, 2019 (accessed on 16 May 2019).
- ↑ Marlin Open-Source RepRap Firmware (accessed on 16 May 2019).
- ↑ Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.
- ↑ F. Thewihsen, S. Karevska, A. Czok, C. Pateman-Jones, D. Krauss. EY’s Global 3D printing Report, 2016.
- ↑ R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.
- ↑ Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.