Coded aperture compressive 3-D LIDAR

Continuous improvement in optical sensing components, as well as recent advances in signal acquisition theory provide a great opportunity to reduce the cost and enhance the capabilities of depth sensing systems. In this paper we propose a new depth sensing architecture that exploits a fixed coded aperture to significantly reduce the number of sensors compared to conventional systems. We further develop a modeling and reconstruction framework, based on model-based compressed sensing, which characterizes a large variety of depth sensing systems. Our experiments demonstrate that it is possible to reduce the number of sensors by more than 85%, with negligible reduction on the sensing quality.

[1]  Robert W. Boyd,et al.  Compressive sensing LIDAR for 3D imaging , 2011, CLEO: 2011 - Laser Science to Photonic Applications.

[2]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[3]  Vivek K Goyal,et al.  Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor. , 2011, Optics express.

[4]  Anton Osokin,et al.  Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  M E Gehm,et al.  Single-shot compressive spectral imaging with a dual-disperser architecture. , 2007, Optics express.

[6]  Mike E. Davies,et al.  Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.

[7]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[8]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[9]  Piotr Indyk,et al.  Approximation-Tolerant Model-Based Compressive Sensing , 2014, SODA.

[10]  Ramesh Raskar,et al.  3D imaging with time of flight cameras: theory, algorithms and applications , 2014, SIGGRAPH '14.

[11]  Ramesh Raskar,et al.  Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing , 2007, SIGGRAPH 2007.

[12]  Ramesh Raskar,et al.  3D Depth Cameras in Vision: Benefits and Limitations of the Hardware , 2014 .

[13]  Pushmeet Kohli,et al.  Exact inference in multi-label CRFs with higher order cliques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Vivek K. Goyal,et al.  CoDAC: A compressive depth acquisition camera framework , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Christoph S. Garbe,et al.  Denoising Time-Of-Flight Data with Adaptive Total Variation , 2011, ISVC.

[17]  Hiroshi Ishikawa,et al.  Exact Optimization for Markov Random Fields with Convex Priors , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Petros Boufounos,et al.  Depth sensing using active coherent illumination , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Deanna Needell,et al.  Stable Image Reconstruction Using Total Variation Minimization , 2012, SIAM J. Imaging Sci..

[20]  Richard G. Baraniuk,et al.  Random Projections of Smooth Manifolds , 2009, Found. Comput. Math..

[21]  Petros Boufounos.  Compressive Sensing for over-the-air ultrasound , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Antonin Chambolle,et al.  Total Variation Minimization and a Class of Binary MRF Models , 2005, EMMCVPR.

[23]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[24]  Ramesh Raskar,et al.  Coded time of flight cameras , 2013, ACM Trans. Graph..

[25]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[26]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[27]  Piotr Indyk,et al.  Approximation Algorithms for Model-Based Compressive Sensing , 2014, IEEE Transactions on Information Theory.

[28]  Volkan Cevher,et al.  Model-Based Compressive Sensing , 2008, IEEE Transactions on Information Theory.