@inproceedings{066eb6542ccd4ee394ac0c9c4372a883,
title = "Compressive depth map acquisition using a single photon-counting detector: Parametric signal processing meets sparsity",
abstract = "Active range acquisition systems such as light detection and ranging (LIDAR) and time-of-flight (TOF) cameras achieve high depth resolution but suffer from poor spatial resolution. In this paper we introduce a new range acquisition architecture that does not rely on scene raster scanning as in LIDAR or on a two-dimensional array of sensors as used in TOF cameras. Instead, we achieve spatial resolution through patterned sensing of the scene using a digital micromirror device (DMD) array. Our depth map reconstruction uses parametric signal modeling to recover the set of distinct depth ranges present in the scene. Then, using a convex program that exploits the sparsity of the Laplacian of the depth map, we recover the spatial content at the estimated depth ranges. In our experiments we acquired 64×64-pixel depth maps of fronto-parallel scenes at ranges up to 2.1 M using a pulsed laser, a DMD array and a single photon-counting detector. We also demonstrated imaging in the presence of unknown partially-transmissive occluders. The prototype and results provide promising directions for non-scanning, low-complexity range acquisition devices for various computer vision applications.",
author = "Andrea Colaco and Ahmed Kirmani and Howland, {Gregory A.} and Howell, {John C.} and Goyal, {Vivek K.}",
year = "2012",
doi = "10.1109/CVPR.2012.6247663",
language = "American English",
isbn = "9781467312264",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
pages = "96--102",
booktitle = "2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012",
note = "2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 ; Conference date: 16-06-2012 Through 21-06-2012",
}