Abstract
As is shown in previous chapters, active lighting is a powerful tool that satisfies various demands. Succeeding chapters provide several application examples of active lighting. Projecting active-lighting images onto real objects augments their appearance. Such projection mapping is now widely used in various fields, including the entertainment industry. Actively lighting multispectral lights onto oil paintings enables novel art modifications. Multispectral light can deceive human eyes because of our RGB limitations, because the light is represented using continuous wavelengths. Therefore, multispectral light can augment the visualization of real objects. Active lighting also enables us to capture the depth of the human body. Thus, a human pose can be estimated from the analysis of its depth. To represent a digital character, capturing actual human motion enables realistic duplication. Estimating human body positions is necessary for representing the motion of digital character and user positions in augmented-, virtual-, and mixed-reality systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raskar R, Welch G, Low KL, Bandyopadhyay D (2001) Shader lamps: animating real objects with image-based illumination. In: Rendering techniques. Springer, pp 89–102
Mine MR, Van Baar J, Grundhofer A, Rose D, Yang B (2012) Projection-based augmented reality in disney theme parks. Computer 45(7):32–40
Bimber O, Gatesy SM, Witmer LM, Raskar R, Encarnação LM (2002) Merging fossil specimens with computer-generated information. Computer 9:25–30
Mukaigawa Y, Nishiyama M, Shakunaga T (2004) Virtual photometric environment using projector. In: Proceedings of the international conference on virtual systems and multimedia, pp 544–553
Hamasaki T, Itoh Y, Hiroi Y, Iwai D, Sugimoto M (2018) Hysar: hybrid material rendering by an optical see-through head-mounted display with spatial augmented reality projection. IEEE Trans Vis Comput Graph 24(4):1457–1466
Raskar R, Beardsley P, van Baar J, Wang Y, Dietz P, Lee J, Leigh D, Willwacher T (2004) Rfig lamps: interacting with a self-describing world via photosensing wireless tags and projectors. In: ACM transactions on graphics (TOG), vol 23. ACM, pp 406–415
Hiura S, Tojo K, Inokuchi S (2003) 3-d tele-direction interface using video projector. In: ACM SIGGRAPH 2003 sketches & applications. ACM, pp 1–1
Kawabe T, Fukiage T, Sawayama M, Nishida S (2016) Deformation lamps: a projection technique to make static objects perceptually dynamic. ACM Trans Appl Percept (TAP) 13(2):10
Punpongsanon P, Iwai D, Sato K (2018) Flexeen: visually manipulating perceived fabric bending stiffness in spatial augmented reality. IEEE Trans Vis Comput Graph
Iwai D, Aoki M, Sato K (2018) Non-contact thermo-visual augmentation by IR-RGB projection. IEEE Trans Vis Comput Graph 25(4):1707–1716
Punpongsanon P, Iwai D, Sato K (2015) Projection-based visualization of tangential deformation of nonrigid surface by deformation estimation using infrared texture. Virtual Real 19(1):45–56
Punpongsanon P, Iwai D, Sato K (2015) Softar: visually manipulating haptic softness perception in spatial augmented reality. IEEE Trans Vis Comput Graph 21(11):1279–1288
Kanamori T, Iwai D, Sato K (2018) Pseudo-shape sensation by stereoscopic projection mapping. IEEE Access 6:40649–40655
Mine R, Iwai D, Hiura S, Sato K (2017) Shape optimization of fabricated transparent layer for pixel density uniformalization in non-planar rear projection. In: Proceedings of the 1st annual ACM symposium on computational fabrication. ACM, p 16
Asayama H, Iwai D, Sato K (2017) Fabricating diminishable visual markers for geometric registration in projection mapping. IEEE Trans Vis Comput Graph 24(2):1091–1102
Amano T (2012) Shading illusion: a novel way for 3-d representation on paper media. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) workshops. IEEE, pp 1–6
Okutani N, Takezawa T, Iwai D, Sato K (2018) Stereoscopic capture in projection mapping. IEEE Access 6:65894–65900
Park JI, Lee MH, Grossberg MD, Nayar SK (2007) Multispectral imaging using multiplexed illumination. In: Proceedings of the international conference on computer vision (ICCV). IEEE, pp 1–8
Bala R, Braun KM, Loce RP (2009) Watermark encoding and detection using narrowband illumination. In: Color and imaging conference, vol 2009. Society for Imaging Science and Technology, pp 139–142
Miyazaki D, Nakamura M, Baba M, Furukawa R, Hiura S (2016) Optimization of led illumination for generating metamerism. J Imaging Sci Technol 60(6):1–60502
Jancosek M, Pajdla T (2011) Multi-view reconstruction preserving weakly-supported surfaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3121–3128
Furukawa Y, Ponce J (2009) Accurate, dense, and robust multiview stereopsis. IEEE Trans Pattern Anal Mach Intell (PAMI) 32(8):1362–1376
Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. In: Sensor fusion IV: control paradigms and data structures, vol 1611. International Society for Optics and Photonics, pp 586–606
Izadi S, Kim D, Hilliges O, Molyneaux D, Newcombe R, Kohli P, Shotton J, Hodges S, Freeman D, Davison A, Fitzgibbon A (2011) Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In: Proceedings of the ACM symposium on user interface software and technology (UIST), pp 559–568
Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohi P, Shotton J, Hodges S, Fitzgibbon A (2011) Kinectfusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE international symposium on mixed and augmented reality. IEEE, pp 127–136
Whelan T, McDonald J, Kaess M, Fallon M, Johannsson H, Leonard J (2010) Kintinuous: Spatially extended KinectFusion. In: Proceedings of the RSS workshop on RGB-D: advanced reasoning with depth cameras
Amberg B, Romdhani S, Vetter T (2007) Optimal step nonrigid ICP algorithms for surface registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8
Li H, Sumner RW, Pauly M (2008) Global correspondence optimization for non-rigid registration of depth scans, pp 1421–1430
Zollhöfer M, Nießner M, Izadi S, Rehmann C, Zach C, Fisher M, Wu C, Fitzgibbon A, Loop C, Theobalt C et al (2014) Real-time non-rigid reconstruction using an RGB-D camera. ACM Trans Graph (TOG) 33(4):1–12
Newcombe RA, Fox D, Seitz SM (2015) Dynamicfusion: reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 343–352
Li H, Adams B, Guibas LJ, Pauly, M.: Robust single-view geometry and motion reconstruction. ACM Trans Graph (TOG) 28(5):175:1–175:10
Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J (2005) Scape: shape completion and animation of people. In: ACM transactions on graphics (TOG), vol 24. ACM, pp 408–416
Chen Y, Liu Z, Zhang Z (2013) Tensor-based human body modeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 105–112
Bogo F, Black MJ, Loper M, Romero J (2015) Detailed full-body reconstructions of moving people from monocular RGB-D sequences. In: Proceedings of the international conference on computer vision (ICCV), pp 2300–2308
Loper M, Mahmood N, Romero J, Pons-Moll G, Black MJ (2015) Smpl: a skinned multi-person linear model. ACM Trans Graph (TOG) 34(6):1–16
Pishchulin L, Wuhrer S, Helten T, Theobalt C, Schiele B (2015) Building statistical shape spaces for 3d human modeling, pp 1–10. arXiv:1503.05860
Kimura R, Sayo A, Dayrit FL, Nakashima Y, Kawasaki H, Blanco A, Ikeuchi K (2018) Representing a partially observed non-rigid 3d human using eigen-texture and eigen-deformation. In: Proceedings of the international conference on pattern recognition (ICPR). IEEE, pp 1043–1048
Sayo A, Onizuka H, Thomas D, Nakashima Y, Kawasaki H, Ikeuchi K (2019) Human shape reconstruction with loose clothes from partially observed data by pose specific deformation. In: Pacific-Rim symposium on image and video technology (PSVIT). Springer, pp 225–239
Nishino K, Sato Y, Ikeuchi K (2002) Eigen-texture method: appearance compression and synthesis based on a 3d model. IEEE Trans Pattern Anal Mach Intell (PAMI) 23(11):1257–1265
Nakashima Y, Okura F, Kawai N, Kimura R, Kawasaki H, Ikeuchi K, Blanco A (2017) Realtime novel view synthesis with eigen-texture regression. In: Proceedings of the British machine vision conference (BMVC)
Bogo F, Kanazawa A, Lassner C, Gehler P, Romero J, Black MJ (2016) Keep it smpl: automatic estimation of 3d human pose and shape from a single image. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 561–578
Sorkine O, Cohen-Or D, Lipman Y, Alexa M, Rössl C, Seidel HP (2004) Laplacian surface editing. In: Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, pp 175–184
Nealen A, Igarashi T, Sorkine O, Alexa M (2006) Laplacian mesh optimization. In: Proceedings of the 4th international conference on computer graphics and interactive techniques in Australasia and Southeast Asia, pp 381–389
Yang J, Franco JS, Hétroy-Wheeler F, Wuhrer S (2018) Analyzing clothing layer deformation statistics of 3d human motions. In: Proceedings of the European conference on computer vision (ECCV), pp 237–253
Vlasic D, Baran I, Matusik W, Popović J (2008) Articulated mesh animation from multi-view silhouettes. ACM Trans Graph (TOG) 27(3):97:1–97:9. https://doi.org/10.1145/1360612.1360696
Yu T, Zheng Z, Guo K, Zhao J, Dai Q, Li H, Pons-Moll G, Liu Y (2018) Doublefusion: real-time capture of human performances with inner body shapes from a single depth sensor. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 7287–7296
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ikeuchi, K. et al. (2020). Visualization/AR/VR/MR Systems. In: Active Lighting and Its Application for Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-56577-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-56577-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-56576-3
Online ISBN: 978-3-030-56577-0
eBook Packages: Computer ScienceComputer Science (R0)