3D visualization of single images using patch level depth

In this paper we consider the task of 3D photo visualization using a single monocular image. The main idea is to use single photos taken by capturing devices such as ordinary cameras, mobile phones, tablet PCs etc. and visualize them in 3D on normal displays. Supervised learning approach is hired to retrieve depth information from single images. This algorithm is based on the hierarchical multi-scale Markov Random Field (MRF) which models the depth based on the multi-scale global and local features and relation between them in a monocular image. Consequently, the estimated depth image is used to allocate the specified depth parameters for each pixel in the 3D map. Accordingly, the multi-level depth adjustments and coding for color anaglyphs is performed. Our system receives a single 2D image as input and provides a anaglyph coded 3D image in output. Depending on the coding technology the special low-cost anaglyph glasses for viewers will be used.

[1]  M. Quartulli,et al.  Bayesian model based city reconstruction from high resolution ISAR data , 2001, IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482).

[2]  Eric Dubois.  A projection method to generate anaglyph stereo images , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  Ashutosh Saxena,et al.  Learning Depth from Single Monocular Images , 2005, NIPS.

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

[5]  Pawan Sinha,et al.  Top-down influences on stereoscopic depth-perception , 1998, Nature Neuroscience.

[6]  Atsuto Maki,et al.  Geotensity: combining motion and lighting for 3D surface reconstruction , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Ashutosh Saxena,et al.  Make3D: Learning 3D Scene Structure from a Single Still Image , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  K. Kinzler,et al.  Perceiving distance accurately by a directional process of integrating ground information , 2022 .

[9]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[10]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[11]  Graham R Jones,et al.  Controlling perceived depth in stereoscopic images , 2001, IS&T/SPIE Electronic Imaging.

[12]  Masaaki Ikehara,et al.  HMM-based surface reconstruction from single images , 2002, Proceedings. International Conference on Image Processing.

[13]  Ashutosh Saxena,et al.  Learning 3-D Scene Structure from a Single Still Image , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Subhasis Chaudhuri,et al.  Depth From Defocus: A Real Aperture Imaging Approach , 1999, Springer New York.

[15]  David F. McAllister,et al.  Methods for computing color anaglyphs , 2010, Electronic Imaging.

[16]  Tony Lindeberg,et al.  Shape from texture from a multi-scale perspective , 1993, 1993 (4th) International Conference on Computer Vision.

[17]  Nicolas S. Holliman,et al.  Mapping perceived depth to regions of interest in stereoscopic images , 2004, IS&T/SPIE Electronic Imaging.

[18]  Narendra Ahuja,et al.  Performance Analysis of Stereo, Vergence, and Focus as Depth Cues for Active Vision , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  R. Rozenholtz,et al.  Computing local surface orientation and shape from texture , 1997 .

[20]  Ashutosh Saxena,et al.  High speed obstacle avoidance using monocular vision and reinforcement learning , 2005, ICML.

[21]  Ping-Sing Tsai,et al.  Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[23]  Vu M Tran.  New methods for rendering of anaglyph stereoscopic images on CRT displays and photo-quality ink-jet printers , 2006 .