Abstract
Obtaining 3D surface information and physical material information of an object from images is an essential research prospect in computer vision and computer graphics. Image-based 3D reconstruction is to extract the 3D depth information of the scene and objects from single or multiple images through specific algorithms to reconstruct the 3D model of objects or locations with robust realism, which has fast reconstruction speed, simple equipment, realistic effect, and minor technical data, which can better realize the virtualization of natural objects.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Song, P., Wu, X., Wang, M.Y.: A robust and accurate method for visual hull computation. In: 2009 International Conference on Information and Automation, pp. 784–789. IEEE (2009)
Rakitina, E., Rakitin, I., Staleva, V., Arnaoutoglou, F., Koutsoudis, A., Pavlidis, G.: An overview of 3D laser scanning technology. In: Proceedings of the International Scientific Conference (2008)
Hosseini, S.J., Araujo, H.: A ToF-aided approach to 3D mesh-based reconstruction of isometric surfaces. In: International Conference on Pattern Recognition Applications and Methods, pp. 146–161. Springer, Cham (2014)
Hou, Z., Su, X., Zhang, Q.: 3D shape compression based on virtual structural light encoding. Acta Optica Sinica 31(5) (2011)
Martin, W.N., Aggarwal, J.K.: Volumetric descriptions of objects from multiple views. IEEE Trans. Pattern Anal. Mach. Intell. 5(2), 150–158 (1983)
Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. Int. J. Comput. Vision 9(2), 137–154 (1992)
Witkin, A.P.: Recovering surface shape and orientation from texture. Artif. Intell. 17(1–3), 17–45 (1981)
Nayar, S.K., Nakagawa, Y.: Shape from focus: An effective approach for rough surfaces. In: Proceedings IEEE International Conference on Robotics and Automation, pp. 218–225. IEEE (1990)
Woodham, R.J.: Photometric stereo: a reflectance map technique for determining surface orientation from image intensity. In: Image Understanding Systems and Industrial Applications I, vol. 155, pp. 136–143. International Society for Optics and Photonics (1979)
Chen, X.R., Cai, P., Shi, W.K.: The latest development of optical non-contact 3D profile measurement. Opt. Precis. Eng. 10(5), 528–532 (2002)
Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. In: European Conference on Computer Vision, pp. 82–96. Springer, Berlin, Heidelberg (2002)
Zabulis, X., Daniilidis, K.J.I.: Multi-camera reconstruction based on surface normal estimation and best viewpoint selection. In: Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2004, pp. 733–740. IEEE (2004)
Lange, R., Seitz, P.: Solid-state time-of-flight range camera. IEEE J. Q. Electron. 37(3), 390–397 (2001)
Ringaby, E., Forssén, P. E.: Scan rectification for structured light range sensors with rolling shutters. In: 2011 International Conference on Computer Vision, ICCV 2011, pp. 1575–1582. IEEE (2011)
Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Trans. Pattern Anal. Mach. Intell. 16(2), 150–162 (1994)
Warren, P.A., Mamassian, P.: Recovery of surface pose from texture orientation statistics under perspective projection. Biol. Cybern. 103(3), 199–212 (2010)
Schmid, K., Hirschmüller, H.: Stereo Vision. Icra (2013)
Kim, Y.M., Theobalt, C., Diebel, J., Kosecka, J., Miscusik, B., Thrun, S.: Multi-view image and ToF sensor fusion for dense 3D reconstruction. In: IEEE International Conference on Computer Vision Workshops, pp. 1542–1549 (2009)
Santo, H., Samejima, M., Sugano, Y., Shi, B., Matsushita, Y.: Deep Photometric Stereo Network. In: IEEE International Conference on Computer Vision Workshop, pp. 501–509 (2017)
Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(1), 2287–2318 (2016)
Niu, C., Li, J., Xu, K.: Im2Struct: recovering 3D shape structure from a single RGB image. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4521–4529 (2018)
Chen, G. Y., Han, K., Wong, K.K.: TOM-Net: learning transparent object matting from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9233–9241 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fang, D., Qin, Z., Liang, S., Luo, R. (2022). Image-Based Physics Rendering for 3D Surface Reconstruction: A Survey. In: Jain, L.C., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 297. Springer, Singapore. https://doi.org/10.1007/978-981-19-2448-4_13
Download citation
DOI: https://doi.org/10.1007/978-981-19-2448-4_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2447-7
Online ISBN: 978-981-19-2448-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)