Multiview radial catadioptric imaging for scene capture

In this paper, we present a class of imaging systems, called radial imaging systems, that capture a scene from a large number of view-points within a single image, using a camera and a curved mirror. These systems can recover scene properties such as geometry, reflectance, and texture. We derive analytic expressions that describe the properties of a complete family of radial imaging systems, including their loci of viewpoints, fields of view, and resolution characteristics. We have built radial imaging systems that, from a single image, recover the frontal 3D structure of an object, generate the complete texture map of a convex object, and estimate the parameters of an analytic BRDF model for an isotropic material. In addition, one of our systems can recover the complete geometry of a convex object by capturing only two images. These results show that radial imaging systems are simple, effective, and convenient devices for a wide range of applications in computer graphics and computer vision.

[1]  Takeo Kanade,et al.  A stereo machine for video-rate dense depth mapping and its new applications , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Richard Szeliski,et al.  The lumigraph , 1996, SIGGRAPH.

[3]  Shree K. Nayar,et al.  Stereo with mirrors , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Shmuel Peleg,et al.  Panoramic mosaics by manifold projection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[6]  Steven M. Seitz,et al.  The Space of All Stereo Images , 2004, International Journal of Computer Vision.

[7]  Ruzena Bajcsy,et al.  High resolution catadioptric omni-directional stereo sensor for robot vision , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[8]  Shree K. Nayar,et al.  Planar catadioptric stereo: geometry and calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[9]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[10]  Alexei A. Efros,et al.  Image quilting for texture synthesis and transfer , 2001, SIGGRAPH.

[11]  Ken Perlin,et al.  Measuring bidirectional texture reflectance with a kaleidoscope , 2003, ACM Trans. Graph..

[12]  Kristin J. Dana BRDF/BTF measurement device , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Takeo Kanade,et al.  Virtualized Reality: Constructing Virtual Worlds from Real Scenes , 1997, IEEE Multim..

[14]  Marc Levoy,et al.  Synthetic aperture confocal imaging , 2004, SIGGRAPH 2004.

[15]  Paul Debevec,et al.  Acquisition of time-varying participating media , 2005, SIGGRAPH 2005.

[16]  Irfan A. Essa,et al.  Graphcut textures: image and video synthesis using graph cuts , 2003, ACM Trans. Graph..

[17]  Anup Basu,et al.  Panoramic stereo , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[18]  Gregory J. Ward,et al.  Measuring and modeling anisotropic reflection , 1992, SIGGRAPH.

[19]  Andrew Gardner,et al.  Capturing and Rendering with Incident Light Fields , 2003, Rendering Techniques.

[20]  Harry Shum,et al.  Rendering with concentric mosaics , 1999, SIGGRAPH.

[21]  Alexei A. Efros,et al.  Texture synthesis by non-parametric sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  Harry Shum,et al.  Synthesizing bidirectional texture functions for real-world surfaces , 2001, SIGGRAPH.