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LidarBoost: Depth Superresolution for ToF 3D Shape Scanning
| Citations: | 29 - 3 self |
BibTeX
@MISC{Schuon_lidarboost:depth,
author = {Sebastian Schuon and Christian Theobalt and James Davis and Sebastian Thrun},
title = {LidarBoost: Depth Superresolution for ToF 3D Shape Scanning},
year = {}
}
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Abstract
Depth maps captured with time-of-flight cameras have very low data quality: the image resolution is rather limited and the level of random noise contained in the depth maps is very high. Therefore, such flash lidars cannot be used out of the box for high-quality 3D object scanning. To solve this problem, we present LidarBoost, a 3D depth superresolution method that combines several low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image. We have developed an optimization framework that uses a data fidelity term and a geometry prior term that is tailored to the specific characteristics of flash lidars. We demonstrate both visually and quantitatively that LidarBoost produces better results than previous methods from the literature. 1.
Keyphrases
depth superresolution flash lidar depth map optimization framework image resolution high-resolution depth image geometry prior term time-of-flight camera depth superresolution method specific characteristic data fidelity term object scanning random noise static scene previous method low data quality
