Abstract
We provide an overview of the concerns, current practice, and limitations for capturing, reconstructing, and representing the real world visually within virtual reality. Given that our goals are to capture, transmit, and depict complex real-world phenomena to humans, these challenges cover the opto-electro-mechanical, computational, informational, and perceptual fields. Practically producing a system for real-world VR capture requires navigating a complex design space and pushing the state of the art in each of these areas. As such, we outline several promising directions for future work to improve the quality and flexibility of real-world VR capture systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aggarwal, R., Vohra, A., Namboodiri, A.M.: Panoramic stereo videos with a single camera. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3755–3763, June 2016. https://doi.org/10.1109/CVPR.2016.408
Aliev, K.A., Ulyanov, D., Lempitsky, V.: Neural point-based graphics (2019). arXiv:1906.08240
Anderson, R., et al.: Jump: virtual reality video. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 35(6), 198:1–198:13 (2016). https://doi.org/10.1145/2980179.2980257
Bau, D., et al.: Seeing what a GAN cannot generate. In: Proceedings of the International Conference on Computer Vision (ICCV) (2019)
Bertel, T., Campbell, N.D.F., Richardt, C.: MegaParallax: casual 360\(^\circ \) panoramas with motion parallax. IEEE Trans. Visual Comput. Graphics 25(5), 1828–1835 (2019). https://doi.org/10.1109/TVCG.2019.2898799
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1) (2007). https://doi.org/10.1007/s11263-006-0002-3
Buehler, C., Bosse, M., McMillan, L., Gortler, S., Cohen, M.: Unstructured lumigraph rendering. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 425–432 (2001). https://doi.org/10.1145/383259.383309
Bussone, W.: Linear and angular head accelerations in daily life. Ph.D. thesis, Virginia Tech (2005)
Cabral, B.: VR capture: designing and building an open source 3D-360 video camera. In: SIGGRAPH Asia Keynote, December 2016
Chai, J.X., Tong, X., Chan, S.C., Shum, H.Y.: Plenoptic sampling. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 307–318 (2000). https://doi.org/10.1145/344779.344932
Chaurasia, G., Duchêne, S., Sorkine-Hornung, O., Drettakis, G.: Depth synthesis and local warps for plausible image-based navigation. ACM Trans. Graph. 32(3), 30:1–30:12 (2013). https://doi.org/10.1145/2487228.2487238
Chaurasia, G., Sorkine-Hornung, O., Drettakis, G.: Silhouette-aware warping for image-based rendering. Comput. Graph. Forum (Proc. Eurographics Symp. Rendering) 30(4), 1223–1232 (2011). https://doi.org/10.1111/j.1467-8659.2011.01981.x
Chen, S.E., Williams, L.: View interpolation for image synthesis. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 279–288 (1993). https://doi.org/10.1145/166117.166153
Cohen, T.S., Welling, M.: Transformation properties of learned visual representations. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)
Collet, A., et al.: High-quality streamable free-viewpoint video. ACM Trans. Graph. (Proc. SIGGRAPH) 34(4), 69:1–69:13 (2015). https://doi.org/10.1145/2766945
Curless, B., Seitz, S., Bouguet, J.Y., Debevec, P., Levoy, M., Nayar, S.K.: 3D photography. In: SIGGRAPH Courses (2000). http://www.cs.cmu.edu/~seitz/course/3DPhoto.html
Dai, A., Nießner, M., Zollhöfer, M., Izadi, S., Theobalt, C.: BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface reintegration. ACM Trans. Graph. 36(3), 24:1–24:18 (2017). https://doi.org/10.1145/3054739
Davis, A., Levoy, M., Durand, F.: Unstructured light fields. Comput. Graph. Forum (Proc. Eurographics) 31(2), 305–314 (2012). https://doi.org/10.1111/j.1467-8659.2012.03009.x
Debevec, P.: The light stages and their applications to photoreal digital actors. In: SIGGRAPH Asia Technical Briefs (2012)
Debevec, P., Bregler, C., Cohen, M.F., McMillan, L., Sillion, F., Szeliski, R.: Image-based modeling, rendering, and lighting. In: SIGGRAPH Courses (2000). https://www.pauldebevec.com/IBMR99/
Debevec, P.E., Taylor, C.J., Malik, J.: Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 11–20, August 1996. https://doi.org/10.1145/237170.237191
Dosovitskiy, A., Springenberg, J.T., Tatarchenko, M., Brox, T.: Learning to generate chairs, tables and cars with convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 692–705 (2017). https://doi.org/10.1109/TPAMI.2016.2567384
DXOMARK: RED Helium 8K DxOMark sensor score: 108—a new all-time-high score! https://www.dxomark.com/red-helium-8k-dxomark-sensor-score-108-a-new-all-time-high-score2/. Accessed 30 Oct 2019
Eslami, S.M.A., et al.: Neural scene representation and rendering. Science 360(6394), 1204–1210 (2018). https://doi.org/10.1126/science.aar6170
Flynn, J., et al.: DeepView: view synthesis with learned gradient descent. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2367–2376, June 2019
Flynn, J., Neulander, I., Philbin, J., Snavely, N.: DeepStereo: learning to predict new views from the world’s imagery. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5515–5524, June 2016. https://doi.org/10.1109/CVPR.2016.595
Fuhrmann, S., Langguth, F., Goesele, M.: MVE: a multi-view reconstruction environment. In: Proceedings of the Eurographics Workshop on Graphics and Cultural Heritage, pp. 11–18 (2014). https://doi.org/10.2312/gch.20141299
Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 873–881, December 2015. https://doi.org/10.1109/ICCV.2015.106
Garon, M., Sunkavalli, K., Hadap, S., Carr, N., Lalonde, J.F.: Fast spatially-varying indoor lighting estimation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 43–54, August 1996. https://doi.org/10.1145/237170.237200
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004). https://doi.org/10.1017/CBO9780511811685
Hedman, P., Alsisan, S., Szeliski, R., Kopf, J.: Casual 3D photography. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 36(6), 234:1–234:15 (2017). https://doi.org/10.1145/3130800.3130828
Hedman, P., Kopf, J.: Instant 3D photography. ACM Trans. Graph. (Proc. SIGGRAPH) 37(4), 101:1–101:12 (2018). https://doi.org/10.1145/3197517.3201384
Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G.: Deep blending for free-viewpoint image-based rendering. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 37(6), 257:1–257:15 (2018). https://doi.org/10.1145/3272127.3275084
Hedman, P., Ritschel, T., Drettakis, G., Brostow, G.: Scalable inside-out image-based rendering. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 35(6), 231:1–231:11 (2016). https://doi.org/10.1145/2980179.2982420
Huang, J., Chen, Z., Ceylan, D., Jin, H.: 6-DOF VR videos with a single 360-camera. In: Proceedings of IEEE Virtual Reality (VR), pp. 37–44, March 2017. https://doi.org/10.1109/VR.2017.7892229
Huang, P.H., Matzen, K., Kopf, J., Ahuja, N., Huang, J.B.: DeepMVS: learning multi-view stereopsis. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Hukkelås, H., Mester, R., Lindseth, F.: DeepPrivacy: a generative adversarial network for face anonymization. In: Bebis, G., et al. (eds.) ISVC 2019. LNCS, vol. 11844, pp. 565–578. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33720-9_44
Ishiguro, H., Yamamoto, M., Tsuji, S.: Omni-directional stereo. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 257–262 (1992). https://doi.org/10.1109/34.121792
Jahanian, A., Chai, L., Isola, P.: On the “steerability” of generative adversarial networks (2019). arXiv:1907.07171
Jancosek, M., Pajdla, T.: Multi-view reconstruction preserving weakly-supported surfaces. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3121–3128, June 2011. https://doi.org/10.1109/CVPR.2011.5995693
Ji, D., Kwon, J., McFarland, M., Savarese, S.: Deep view morphing. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7092–7100, July 2017. https://doi.org/10.1109/CVPR.2017.750
Kalantari, N.K., Wang, T.C., Ramamoorthi, R.: Learning-based view synthesis for light field cameras. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 35(6), 193:1–193:10 (2016). https://doi.org/10.1145/2980179.2980251
Keysers, C., Xiao, D.K., Földiák, P., Perrett, D.I.: The speed of sight. J. Cogn. Neurosci. 13(1), 90–101 (2001). https://doi.org/10.1162/089892901564199
Kim, C., Zimmer, H., Pritch, Y., Sorkine-Hornung, A., Gross, M.: Scene reconstruction from high spatio-angular resolution light fields. ACM Trans. Graph. (Proc. SIGGRAPH) 32(4), 73:1–73:12 (2013). https://doi.org/10.1145/2461912.2461926
Kim, H., et al.: Deep video portraits. ACM Trans. Graph. (Proc. SIGGRAPH) 37(4), 163:1–163:14 (2018). https://doi.org/10.1145/3197517.3201283
Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: Panoptic segmentation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Konrad, R., Dansereau, D.G., Masood, A., Wetzstein, G.: SpinVR: towards live-streaming 3D virtual reality video. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 36(6), 209:1–209:12 (2017). https://doi.org/10.1145/3130800.3130836
Kopf, J., et al.: Practical 3D photography. In: Proceedings of CVPR Workshops (2019)
Koulieris, G.A., Akşit, K., Stengel, M., Mantiuk, R.K., Mania, K., Richardt, C.: Near-eye display and tracking technologies for virtual and augmented reality. Comput. Graph. Forum 38(2), 493–519 (2019). https://doi.org/10.1111/cgf.13654
Kulkarni, T.D., Whitney, W., Kohli, P., Tenenbaum, J.B.: Deep convolutional inverse graphics network. In: Advances in Neural Information Processing Systems (NIPS) (2015)
Lanman, D., Wetzstein, G., Hirsch, M., Heidrich, W., Raskar, R.: Polarization fields: dynamic light field display using multi-layer LCDs. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 30(6), 186:1–186:10 (2011). https://doi.org/10.1145/2070781.2024220
Lee, J., Kim, B., Kim, K., Kim, Y., Noh, J.: Rich360: optimized spherical representation from structured panoramic camera arrays. ACM Trans. Graph. (Proc. SIGGRAPH) 35(4), 63:1–63:11 (2016). https://doi.org/10.1145/2897824.2925983
LeGendre, C., et al.: DeepLight: learning illumination for unconstrained mobile mixed reality. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Levoy, M., Hanrahan, P.: Light field rendering. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 31–42, August 1996. https://doi.org/10.1145/237170.237199
Lipski, C., Linz, C., Berger, K., Sellent, A., Magnor, M.: Virtual video camera: image-based viewpoint navigation through space and time. Comput. Graph. Forum 29(8), 2555–2568 (2010). https://doi.org/10.1111/j.1467-8659.2010.01824.x
Lombardi, S., Simon, T., Saragih, J., Schwartz, G., Lehrmann, A., Sheikh, Y.: Neural volumes: learning dynamic renderable volumes from images. ACM Trans. Graph. (Proc. SIGGRAPH) 38(4), 65:1–65:14 (2019). https://doi.org/10.1145/3306346.3323020
Luo, B., Xu, F., Richardt, C., Yong, J.H.: Parallax360: stereoscopic 360\(^\circ \) scene representation for head-motion parallax. IEEE Trans. Vis. Comput. Graph. 24(4), 1545–1553 (2018). https://doi.org/10.1109/TVCG.2018.2794071
Magnor, M., Grau, O., Sorkine-Hornung, O., Theobalt, C. (eds.): Digital Representations of the Real World: How to Capture, Model, and Render Visual Reality. A K Peters/CRC Press, New York (2015)
Martin-Brualla, R., et al.: LookinGood: enhancing performance capture with real-time neural re-rendering. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 37(6), 255:1–255:14 (2018). https://doi.org/10.1145/3272127.3275099
Meka, A., et al.: Deep reflectance fields: high-quality facial reflectance field inference from color gradient illumination. ACM Trans. Graph. (Proc. SIGGRAPH) 38(4), 77:1–77:12 (2019). https://doi.org/10.1145/3306346.3323027
Meshry, M., et al.: Neural rerendering in the wild. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Mildenhall, B., et al.: Local light field fusion: practical view synthesis with prescriptive sampling guidelines. ACM Trans. Graph. (Proc. SIGGRAPH) 38(4), 29:1–29:14 (2019). https://doi.org/10.1145/3306346.3322980
Mori, M.: The uncanny valley. Energy 7(4), 33–35 (1970). (in Japanese)
Moulon, P., Monasse, P., Marlet, R.: Adaptive structure from motion with a Contrario model estimation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7727, pp. 257–270. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37447-0_20
Mustafa, A., Volino, M., Guillemaut, J.Y., Hilton, A.: 4D temporally coherent light-field video. In: Proceedings of International Conference on 3D Vision (3DV) (2017)
Mustafa, A., Volino, M., Kim, H., Guillemaut, J.Y., Hilton, A.: Temporally coherent general dynamic scene reconstruction (2019). arXiv:1907.08195
Nam, G., Lee, J.H., Gutierrez, D., Kim, M.H.: Practical SVBRDF acquisition of 3D objects with unstructured flash photography. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 37(6), 267:1–267:12 (2018). https://doi.org/10.1145/3272127.3275017
Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136, October 2011. https://doi.org/10.1109/ISMAR.2011.6092378
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: HoloGAN: unsupervised learning of 3D representations from natural images. In: Proceedings of the International Conference on Computer Vision (ICCV) (2019)
Nießner, M., Zollhöfer, M., Izadi, S., Stamminger, M.: Real-time 3D reconstruction at scale using voxel hashing. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 32(6), 169:1–169:11 (2013). https://doi.org/10.1145/2508363.2508374
Niklaus, S., Mai, L., Yang, J., Liu, F.: 3D Ken Burns effect from a single image. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 38(6), 184:1–184:15 (2019). https://doi.org/10.1145/3355089.3356528
Oculus: From the lab to the living room: the story behind Facebook’s Oculus Insight technology and a new era of consumer VR. https://tech.fb.com/the-story-behind-oculus-insight-technology/. Accessed 30 Oct 2019
Olszewski, K., Tulyakov, S., Woodford, O., Li, H., Luo, L.: Transformable bottleneck networks. In: Proceedings of the International Conference on Computer Vision (ICCV) (2019)
Overbeck, R.S., Erickson, D., Evangelakos, D., Pharr, M., Debevec, P.: A system for acquiring, compressing, and rendering panoramic light field stills for virtual reality. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 37(6), 197:1–197:15 (2018). https://doi.org/10.1145/3272127.3275031
Park, E., Yang, J., Yumer, E., Ceylan, D., Berg, A.C.: Transformation-grounded image generation network for novel 3D view synthesis. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 702–711, July 2017. https://doi.org/10.1109/CVPR.2017.82
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Parra Pozo, A., et al.: An integrated 6DoF video camera and system design. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 38(6), 216:1–216:16 (2019). https://doi.org/10.1145/3355089.3356555. https://github.com/facebook/facebook360dep
Peleg, S., Ben-Ezra, M., Pritch, Y.: Omnistereo: panoramic stereo imaging. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 279–290 (2001). https://doi.org/10.1109/34.910880
Penner, E., Zhang, L.: Soft 3D reconstruction for view synthesis. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 36(6), 235:1–235:11 (2017). https://doi.org/10.1145/3130800.3130855
Perazzi, F., et al.: Panoramic video from unstructured camera arrays. Comput. Graph. Forum (Proc. Eurographics) 34(2), 57–68 (2015). https://doi.org/10.1111/cgf.12541
Prada, F., Kazhdan, M., Chuang, M., Collet, A., Hoppe, H.: Spatiotemporal atlas parameterization for evolving meshes. ACM Trans. Graph. (Proc. SIGGRAPH) 36(4), 58:1–58:12 (2017). https://doi.org/10.1145/3072959.3073679
Qi, M., Li, W., Yang, Z., Wang, Y., Luo, J.: Attentive relational networks for mapping images to scene graphs. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Rhodin, H., Salzmann, M., Fua, P.: Unsupervised geometry-aware representation for 3D human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 765–782. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_46
Richardt, C., Hedman, P., Overbeck, R.S., Cabral, B., Konrad, R., Sullivan, S.: Capture4VR: from VR photography to VR video. In: SIGGRAPH Courses (2019). https://doi.org/10.1145/3305366.3328028
Richardt, C., Pritch, Y., Zimmer, H., Sorkine-Hornung, A.: Megastereo: constructing high-resolution stereo panoramas. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1256–1263, June 2013. https://doi.org/10.1109/CVPR.2013.166
Schroers, C., Bazin, J.C., Sorkine-Hornung, A.: An omnistereoscopic video pipeline for capture and display of real-world VR. ACM Trans. Graph. 37(3), 37:1–37:13 (2018). https://doi.org/10.1145/3225150
Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4104–4113 (2016). https://doi.org/10.1109/CVPR.2016.445
Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31
Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 519–528 (2006). https://doi.org/10.1109/CVPR.2006.19
Serrano, A., et al.: Motion parallax for 360\(^\circ \) RGBD video. IEEE Trans. Vis. Comput. Graph. 25(5), 1817–1827 (2019). https://doi.org/10.1109/TVCG.2019.2898757
Shum, H., Kang, S.B.: Review of image-based rendering techniques. In: Proceedings of the SPIE Visual Communications and Image Processing, vol. 4067 (2000). https://doi.org/10.1117/12.386541
Shum, H.Y., Chan, S.C., Kang, S.B.: Image-Based Rendering. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-32668-9
Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., Zollhöfer, M.: DeepVoxels: learning persistent 3D feature embeddings. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2437–2446 (2019)
Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: continuous 3D-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. (Proc. SIGGRAPH) 25(3), 835–846 (2006). https://doi.org/10.1145/1141911.1141964
Speciale, P., Schönberger, J.L., Kang, S.B., Sinha, S.N., Pollefeys, M.: Privacy preserving image-based localization. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Srinivasan, P.P., Tucker, R., Barron, J.T., Ramamoorthi, R., Ng, R., Snavely, N.: Pushing the boundaries of view extrapolation with multiplane images. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Sweeney, C.: Theia multiview geometry library (2016). http://theia-sfm.org
Sweeney, C., Holynski, A., Curless, B., Seitz, S.M.: Structure from motion for panorama-style videos (2019). arXiv:1906.03539
Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends Comput. Graph. Vis. 2(1), 1–104 (2006). https://doi.org/10.1561/0600000009
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, London (2010). https://doi.org/10.1007/978-1-84882-935-0. http://szeliski.org/Book/
Tarko, J., Tompkin, J., Richardt, C.: Real-time virtual object insertion for moving 360\(^\circ \) videos. In: Proceedings of the International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI) (2019)
Tatarchenko, M., Dosovitskiy, A., Brox, T.: Multi-view 3D models from single images with a convolutional network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 322–337. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_20
Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. (Proc. SIGGRAPH) 38(4), 66:1–66:12 (2019). https://doi.org/10.1145/3306346.3323035
Tulsiani, S., Tucker, R., Snavely, N.: Layer-structured 3D scene inference via view synthesis. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 311–327. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_19
Tung, H.Y.F., Cheng, R., Fragkiadaki, K.: Learning spatial common sense with geometry-aware recurrent networks. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2595–2603 (2019)
Valve: Index headset. www.valvesoftware.com/en/index/headset. Accessed 30 Oct 2019
Ventura, J.: Structure from motion on a sphere. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 53–68. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_4
Wei, S.E., et al.: VR facial animation via multiview image translation. ACM Trans. Graph. (Proc. SIGGRAPH) 38(4), 67:1–67:16 (2019). https://doi.org/10.1145/3306346.3323030
Weissig, C., Schreer, O., Eisert, P., Kauff, P.: The ultimate immersive experience: panoramic 3D video acquisition. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 671–681. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27355-1_72
Wetzstein, G., Lanman, D., Heidrich, W., Raskar, R.: Layered 3D: tomographic image synthesis for attenuation-based light field and high dynamic range displays. ACM Trans. Graph. (Proc. SIGGRAPH) 30(4), 95:1–95:12 (2011). https://doi.org/10.1145/2010324.1964990
Wetzstein, G., Lanman, D., Hirsch, M., Raskar, R.: Tensor displays: compressive light field synthesis using multilayer displays with directional backlighting. ACM Trans. Graph. (Proc. SIGGRAPH) 31(4), 80:1–80:11 (2012). https://doi.org/10.1145/2185520.2185576
Whelan, T., Salas-Moreno, R.F., Glocker, B., Davison, A.J., Leutenegger, S.: ElasticFusion: real-time dense SLAM and light source estimation. Int. J. Robot. Res. 35(14), 1697–1716 (2016). https://doi.org/10.1177/0278364916669237
Wood, D.N., et al.: Surface light fields for 3D photography. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 287–296 (2000). https://doi.org/10.1145/344779.344925
Worrall, D.E., Garbin, S.J., Turmukhambetov, D., Brostow, G.J.: Interpretable transformations with encoder-decoder networks. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 5737–5746 (2017). https://doi.org/10.1109/ICCV.2017.611
Wu, C.: VisualSFM: a visual structure from motion system (2011). http://ccwu.me/vsfm/
Yang, J., Reed, S.E., Yang, M.H., Lee, H.: Weakly-supervised disentangling with recurrent transformations for 3D view synthesis. In: Advances in Neural Information Processing Systems (NIPS), pp. 1099–1107 (2015)
Yifan, W., Serena, F., Wu, S., Öztireli, C., Sorkine-Hornung, O.: Differentiable surface splatting for point-based geometry processing. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 38(6) (2019). https://doi.org/10.1145/3355089.3356513
Yücer, K., Sorkine-Hornung, A., Wang, O., Sorkine-Hornung, O.: Efficient 3D object segmentation from densely sampled light fields with applications to 3D reconstruction. ACM Trans. Graph. 35(3), 22:1–22:15 (2016). https://doi.org/10.1145/2876504
Zaragoza, J., Chin, T.J., Tran, Q.H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving DLT. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1285–1298 (2014). https://doi.org/10.1109/TPAMI.2013.247
Zhang, F., Liu, F.: Parallax-tolerant image stitching. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3262–3269, June 2014. https://doi.org/10.1109/CVPR.2014.423
Zheng, K.C., Kang, S.B., Cohen, M.F., Szeliski, R.: Layered depth panoramas. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2007). https://doi.org/10.1109/CVPR.2007.383295
Zhou, T., Tucker, R., Flynn, J., Fyffe, G., Snavely, N.: Stereo magnification: learning view synthesis using multiplane images. ACM Trans. Graph. (Proc. SIGGRAPH) 37(4), 65:1–65:12 (2018). https://doi.org/10.1145/3197517.3201323
Zhou, T., Tulsiani, S., Sun, W., Malik, J., Efros, A.A.: View synthesis by appearance flow. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 286–301. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_18
Zollhöfer, M., et al.: State of the art on monocular 3D face reconstruction, tracking, and applications. Comput. Graph. Forum 37(2), 523–550 (2018). https://doi.org/10.1111/cgf.13382
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Richardt, C., Tompkin, J., Wetzstein, G. (2020). Capture, Reconstruction, and Representation of the Visual Real World for Virtual Reality. In: Magnor, M., Sorkine-Hornung, A. (eds) Real VR – Immersive Digital Reality. Lecture Notes in Computer Science(), vol 11900. Springer, Cham. https://doi.org/10.1007/978-3-030-41816-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-41816-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41815-1
Online ISBN: 978-3-030-41816-8
eBook Packages: Computer ScienceComputer Science (R0)