Christian Beder, Bogumil Bartczak and Reinhard Koch, 2007
Real-time active 3D range cameras based on time-of-flight technology using the Photonic Mixer Device (PMD) can be considered as a complementary technique for stereo-vision based depth estimation. Since those systems directly yield 3D measurements, they can also be used for initializing vision based approaches, especially in highly dynamic environments. Fusion of PMD depth images with passive intensity-based stereo is a promising approach for obtaining reliable surface reconstructions even in weakly textured surface regions. In this work a PMD-stereo fusion algorithm for the estimation of patchlets from a combined PMD-stereo camera rig will be presented. As patchlet we define an oriented small planar 3d patch with associated surface normal. Least-squares estimation schemes for estimating patchlets from PMD range images as well as from a pair of stereo images are derived. It is shown, how those two approaches can be fused into one single estimation, that yields results even if either of the two single approaches fails.
Christian Beder, Bogumil Bartczak and Reinhard Koch
Recently real-time active 3D range cameras based on time-of-flight technology (PMD) have become available. Those cameras can be considered as a competing technique
for stereo-vision based surface reconstruction. Since those systems directly yield accurate 3d measurements, they can be used for benchmarking vision based approaches, especially in highly dynamic environments. Therefore, a comparative study of the two approaches is relevant. In this work the achievable accuracy of the two techniques, PMD and stereo, is compared on the basis of patchlet estimation. As patchlet we define an oriented small planar 3d patch with associated surface normal. Leastsquares estimation schemes for estimating patchlets from PMD range images as well as from a pair of stereo images are derived. It is shown, how the achivable accuracy can be estimated for both systems. Experiments under optimal conditions for both systems are performed and the achievable accuracies are compared. It has been found that the PMD system outperformed the stereo system in terms of achievable accuracy for distance measurements, while the estimation of normal direction is comparable for both systems.
Cang Ye and GuruPrasad M. Hegde
This paper presents a new method for extracting object edges from range images obtained by a 3D range imaging sensor⎯the SwissRanger SR-3000. In range image preprocessing tage, the method enhances object edges by using surface normal information; and it employs the Hough Transform to detect straight line features in the Normal-Enhanced Range Image NERI). Due to the noise in the sensor’s range data, a NERI contains corrupted object surfaces that may result in unwanted edges and greatly encumber the extraction of linear features. To alleviate this problem, a Singular Value Decomposition (SVD) filter is developed to smooth object surfaces. The efficacy of the edge extraction method is validated by experiments in various environments.