Path Types Study

This study kept the viewpoint evaluation method constant, using a combination of rule of thirds and model normals. The variable being studied here was path type. Videos A through E respectively displayed paths with large variation, many nodes, small variation and perfectly circular, largely elliptical, and random. The relevant parameters used to generate each path are summarized in Table 6.3. Excluding random, 30 paths of each type were generated and the best scoring path of each type was used in the study. Since the random path used no evaluation criteria, one random path was generated and used in the study. A summary of the study results can be seen in Table 6.4. This study had 32 participants total.

Table 6.3: Parameters used for each video in the path types study. Video A was a path with large variations, B contained many nodes, C was per- fectly circular with small variation, D was extremely elliptical, and E was random.

Video Nodes in Path Radius Radius Variation Elliptical Height Delta Pitch Delta

A 12 27 +-3 x, z 2.5 7.5◦

B 45 30 +-1 x, 0.8z 1.0 5◦

C 10 23 +-0 x, z 1.5 5◦

D 12 26 +-1.5 1.3x, 0.8z 2.0 5◦

E 12 27 +-1 x, 0.8z 2.5 7.5◦

last two places. Video A, with the largest allowable deltas and radius variations, was the clear winner with 18 of 32 people ranking it number 1 and 12 people ranking it number 2. The camera viewpoints used in this path can be seen in Figure 5.1 in Chapter 5. The perfectly circular path, video C, came in second and video D, with the highly elliptical path came in third. Fourth and fifth place were very close but random, video E, came in fourth while the path with many nodes, video B, came in last.

6.2.2.2 Video Comments

Even though fewer people participated in this study than the last one, there were more comments with an average of 17.4 comments per video. The winning video, video A, received the most positive comments such as “The camera angle was perfect because the blue object was in view the entire time. It was also slow and steady which was easy to look at and gave ample time to view everything within frame”, “A good path with nice camera height that shows some details of the topside of the ship, with a nice ending in stable, bottom-up shot of the bow”, and “Good balance of seeing the rocks, full landscape, and ship”.

Table 6.4: Results from the path types video study. Columns 2 through 6 show the number of participants that put each preference for each video with 1 being most preferred and 5 being least preferred. The last column shows the average score each video received. The winning video was Video A with an average score of 1.50

Preference 1 2 3 4 5 Average

Video A (More variation) 18 12 2 0 0 1.50

Video B (Many nodes) 1 0 2 12 17 4.375

Video C (Circular, small variation) 9 13 9 0 1 2.09

Video D (Elliptical) 3 6 18 4 1 2.81

Video E (Random) 1 1 1 16 13 4.22

Participants noted that they liked video C second best with comments such as “Second best, but I think we don’t get to see the top enough”, “Nicely controlled and sweeping camera movement. Subject stays in frame. Fewer dramatic moments than A”, and “A nice path, but more diversity in camera height as well as a bit overall camera height may reveal more detail on the topside of the ship”.

The elliptical path in video D got a variety of comments such as “Path radius seems unnecessarily large”, “elliptical orbit is a bit confusing”, “I liked the zoom in at the beginning, but don’t think it should have zoomed back out after the fly-by”, and “Longer range shots are nice”.

The random path, video E, received many comments about the ship being cut off including “Centering seems totally off, annoying to not be able to see the object”, “Motion was smooth, but it didn’t focus on the actual object much”, “Subject (ship- wreck) seems to be off-camera for too long”, and “This one makes me think I should

ferent viewpoints with a possible radius variation of 1, a possible height delta of 1.5 and pitch delta of 5◦ this path truly is bumpy. A sample of these comments are “vertical and z motion very distracting”, “Way too much jerky dollying movement (forward-backward)”, “super shaky”, “Too much zooming in and out, kinda giving me a headache”, and “The camera was really wobbly and extremely nauseating to watch. I was immediately put off and didn’t want to watch the rest despite the video only being 14 seconds”.

6.2.2.3 Additional Comments

In the additional comments/feedback section at the end, there was again positive sentiment towards these videos. Comments included “Video A seems to be a “safe” choice for something like an establishing shot, however, I liked D’s sweeping, smooth motion more, though it may be better suited to a dramatic establishment shot”, “Camera A looked really nice. I’d love to see this on an arbitrary mesh”, and “Once again, I prefer the most stable and consistent path”.

Some participants noted that they liked the scene with comments like “Love the water and the ship! Would love if the ship were less reflective to see any holes or indents more easily. But that’s just a personal preference”, “beautiful renders, although the sea floor was a bit tile-y. I wish I could’ve seen the subject from above”, and “The lighting in this scene is really good”.

We can conclude that users strongly preferred our generated paths over random ones but did not like paths with a lot of undulation. They preferred paths that kept the shipwreck in focus the whole time but showed it from a variety of angles. Men- tioned in the future work, Section 7.1, we would like to add a measure of bounciness to calculating our path weight since users preferred large deltas so they could see the

model from many angles without the camera jumping up-and-down or in-and-out too much.

Chapter 7 CONCLUSION

7.1 Future Work

This thesis introduces a novel fly-though generation system based on cinematographic and geometric principles shown to be preferred by users over flat and random paths. However, it has a number of limitations and areas for improvement. Future work includes:

• Implementation of fly-through path generation system with motion planning algorithms other than the PRM.

• Comparison between Catmull-Rom splines and other types of splines such as B-splines for interpolating between nodes.

• Adding more cinematographic principles to path generation rules such as leading lines and visual balance.

• Add measure of jumpiness to path weight calculation and have jumpier paths receive worse scores as this was a main complaint in the user study.

• Test the fly-through generation system with other scenes and different models. • Include the kinematic constraints of an AUV in the node generation process so

the paths could actually be used by a robot to take footage of shipwrecks. • Expand PRM algorithm to work in all six degrees of freedom to generate more

interesting paths.

• Keep the OpenCV image data on the GPU for faster computation, rather than transferring it to the CPU.

7.2 Conclusion

This work automatically generates camera fly-throughs of single model scenes. This is important because hand generating fly-throughs is a laborious, time-consuming task. The example scene used is a underwater scene focused on a 3D re-creation of the X127 shipwreck located in Malta. The model was created through use of an autonomous underwater vehicle and photogrammetry. A probabilistic roadmap algorithm is used to generate camera paths. The roadmap uses a list of “high weight” nodes to gen- erate more ideal paths. The node viewpoints are evaluated on cinematographic and geometric principles, characterized by the rule of thirds and model normals.

Three user studies were preformed to validate different aspects of this system. An initial user study was done validating the rule of thirds as used in our scene. Two studies were done with videos created from our final path generation system using different viewpoint evaulation criteria for one study and different path types for the other. In the first of these two studies, we found that users preferred the paths generated using the rule of thirds over flat and random ones. They strongly preferred all viewpoint evaluation methods used by our system– the rule of thirds, model normals, and a combination of these, over randomly generated paths. The last study also found that users preferred paths generated by our system over random ones except paths with too much bumpiness that made users feel nauseous. They liked paths with few nodes but large deltas so the shipwreck was visible from a variety of angles without too much camera undulation.

BIBLIOGRAPHY

[1] 10 top photography composition rules.

http://www.photographymad.com/pages/view/10-top-photography- composition-rules.

[2] Cal Poly Github. http://www.github.com/CalPoly. [3] Panning best practices.

http://www.red.com/learn/red-101/camera-panning-speed. [4] Tutorial 12: Quaternions. http://www.xojo3d.com/tut012.php. [5] Dive sites: X127 Lighter ”Coralita” (Manoel Island), 2015.

http://www.divesystemsmalta.com/about-malta/dive-sites-x127/. [6] The X127 water lighter (Coralita), 2016.

http://www.dawndiving.com/the-x127-water-lighter-coralita/.

[7] D. B. Christianson, S. E. Anderson, and L. wei He. Declarative camera control for automatic cinematography. American Association for Artificial Intelligence, Dec. 1996.

[8] C. Clark. Harvey Mudd College E160 – lecture 13 autonomous robot navigation, June 2016.

http://www.hmc.edu/lair/E160/E160-Lecture14-MotionPlanning.pdf. [9] L. K. Dale and N. M. Amato. Probabilistic roadmaps-putting it all together. In

Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), volume 2, pages 1940–1947 vol.2, 5 2001.

[10] T. Gambin. A phoenician shipwreck off Gozo, Malta. The Journal of the Archaeological Society, Malta, 2010.

[11] D. Hsu, T. Jiang, J. Reif, and Z. Sun. The bridge test for sampling narrow passages with probabilistic roadmap planners. In 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), volume 3, pages 4420–4426 vol.3, Sept 2003.

[12] A. Huamn. Creating bounding boxes and circles for contours.

http://docs.opencv.org/2.4/doc/tutorials/imgproc/shapedescriptors/ bounding_rects_circles/bounding_rects_circles.html#bounding-rects- circles.

[13] N. Joubert, J. L. E, D. B. Goldman, F. Berthouzoz, M. Roberts, J. A. Landay, and P. Hanrahan. Towards a Drone Cinematographer: Guiding Quadrotor Cameras using Visual Composition Principles. ArXiv e-prints, Oct. 2016. [14] T.-Y. Li and C.-C. Cheng. Real-Time Camera Planning for Navigation in

Virtual Environments, pages 118–129. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.

[15] T.-Y. Li and Y.-C. Shie. An incremental learning approach to motion planning with roadmap management. In Proceedings 2002 IEEE International

Conference on Robotics and Automation (Cat. No.02CH37292), volume 4, pages 3411–3416 vol.4, May 2002.

[16] L. Liu, R. Chen, L. Wolf, and D. Cohen-Or. Optimizing photo composition. Computer Graphics Forum, 29(2):469–478, 2010.

[18] M. Rantanen. Improving Probabilistic Roadmap Methods for Fast Motion Planning. PhD thesis, School of Information Sciences, University of Tampere, Aug. 2014.

[19] V. Setlur, S. Takagi, R. Raskar, M. Gleicher, and B. Gooch. Automatic image retargeting. In Proceedings of the 4th International Conference on Mobile and Ubiquitous Multimedia, MUM ’05, pages 59–68, New York, NY, USA, 2005. ACM.

[20] K. Sijmons. Introduction on photogrammetry. http://drm.cenn.org/ Trainings/Generation%20of%20geodatabases%20using%20ARCGIS%20and% 20ERDAS/Lectures/Introduction%20on%20Photogrammetry.pdf.

[21] V. K. Viswanathan, Z. Lobo, J. Lupanow, S. M. T. S. von Fock, Z. Wood, T. Gambin, and C. Clark. Auv motion-planning for photogrammetric

reconstruction of marine archaeological sites. InProceedings of the 2017 IEEE International Conference on Robotics and Automation, May 2017.

[22] S. M. T. S. von Fock, S. Bilich, K. Davis, V. K. Viswanthan, Z. Lobo,

J. Lupanow, C. Clark, T. Gambin, and Z. Wood. Pipeline for reconstruction and visualization of underwater archaeology sites using photogrammetry. In Proceedings of the 2017 ISCA International Conference on Computers and Their Applications, Mar. 2017.

APPENDICES

Appendix A

Thesis Survey­ Shipwreck Images

Please rank each set of images in order of preference. There is no right or wrong answer­ this is about  what looks best to you! * Required 1. Set 1 * Mark only one oval per row. 1 (most preferred) 2 3 (least preferred) Image 1 Image 2 Image 3

Image 1

Image 2

2. Set 2 * Mark only one oval per row. 1 (most preferred) 2 3 (least preferred) Image A Image B Image C

Image A

Image B

Appendix B

Automatically Generated Virtual Camera Paths:

Evaluation Methods

INFORMED CONSENT TO PARTICIPATE IN A RESEARCH PROJECT, “PROBABILISTIC ROADMAPS  FOR VIRTUAL CAMERA PATHING WITH  CINEMATOGRAPHIC PRINCIPLES”     A research project on using probabilistic roadmaps for generating virtual camera paths with  cinematographic principles is being conducted by graduate student Katie Davis and Dr. Zoë Wood in the  Department of Computer Science at Cal Poly, San Luis Obispo.  The purpose of the study is to validate a  thesis focused on computationally creating animations.    You are being asked to take part in this study by completing the following questionnaire.  You will be  asked to watch a few short videos of less than a minute in length and order them by preference. Your  participation will take approximately five to ten minutes.  Please be aware that you are not required to  participate in this research, you may omit any items that you prefer not to answer, and you may  discontinue your participation at any time without penalty.      There are no risks anticipated with participation in this study. Your responses will be provided  anonymously to protect your privacy.  Potential benefits associated with the study include furthering the  understanding of what makes good camera paths.    If you have questions regarding this study or would like to be informed of the results when the study  is completed, please feel free to contact Katie Davis at [email protected] or Zoë Wood at 

[email protected].  If you have concerns regarding the manner in which the study is conducted, you 

may contact Dr. Michael Black, Chair of the Cal Poly Institutional Review Board, at (805) 756­2894, 

[email protected], or Dr. Dean Wendt, Dean of Research, at (805) 756­1508, [email protected]. 

  If you agree to voluntarily participate in this research project as described, please indicate your  agreement by completing and submitting the following questionnaire.  Please print a copy of this consent  form now for your reference, and thank you for your participation in this research.  * Required 1. Participation Agreement * Mark only one oval.  Yes, I volunteer  No, I do not volunteer  Stop filling out this form. Skip to question 2.

Survey

Evaluation of Cal Poly Masters Thesis about using probabilistic roadmap algorithms to generate virtual  camera paths with cinematographic principles. This survey focuses on different methods of evaluating  camera viewpoints. Videos have no sound. Replay button is in bottom left of every video if you need to  watch them multiple times. If you wish to opt out at any point please close this page.  2. Please rank the following videos by preference * Mark only one oval per row. 1 (most preferred) 2 3 4 5 (least preferred)

3. Video A Comments           http://youtube.com/watch?v=_RenQhdZLkA

Video B

4. Video B Comments           http://youtube.com/watch?v=EjK1VGKeDG0

Video C

5. Video C Comments           http://youtube.com/watch?v=cezK0zCdiZ4

Video D

6. Video D Comments           http://youtube.com/watch?v=1S0SJQS7VR8

Video E

Powered by 7. Video E Comments          

Last Thoughts

8. Additional comments or feedback           http://youtube.com/watch?v=5aovCnCIWRU

The survey of generated paths focusing on different path types is identical to the above survey with different videos. These videos can be found on this youtube channel https://www.youtube.com/channel/UCUH0rkZ8iDLe9LjA2n8DPCA. Links to specific videos are as follows.

Video A: http://youtube.com/watch?v=fpLR5iENRBw

Video B:http://youtube.com/watch?v=sw8GIwJTm94

Video C: http://youtube.com/watch?v=Dwv5gBDGOqQ

Video D:http://youtube.com/watch?v=JW6LxsfddO4

In document Probabilistic Roadmaps for Virtual Camera Pathing with Cinematographic Principles (Page 59-81)