Standardization of intensity-values acquired by Time-of-Flight-cameras

Stürmer, Michael; Penne, Jochen; Hornegger, Joachim

doi:10.1109/cvprw.2008.4563166

Cited by 15 publications

(16 citation statements)

References 8 publications

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“…But for the ToF camera, as the primary light source is around the camera, this could still be feasible and should be investigated. Indeed, Stürmer et al [25] observed that the amplitude image in observed an inverse square falloff with distance. Finally, silhouettes constrain the direction of normals of the depth map to be perpendicular to the pixel ray.…”

Section: Choice Of Depth Cuesmentioning

confidence: 98%

A Survey on Time-of-Flight Stereo Fusion

Nair

Ruhl

Lenzen

et al. 2013

Lecture Notes in Computer Science

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Abstract. Due to the demand for depth maps of higher quality than possible with a single depth imaging technique today, there has been an increasing interest in the combination of different depth sensors to produce a "super-camera" that is more than the sum of the individual parts. In this survey paper, we give an overview over methods for the fusion of Time-of-Flight (ToF) and passive stereo data as well as applications of the resulting high quality depth maps. Additionally, we provide a tutorial-based introduction to the principles behind ToF stereo fusion and the evaluation criteria used to benchmark these methods.

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Section: Choice Of Depth Cuesmentioning

confidence: 98%

A Survey on Time-of-Flight Stereo Fusion

Nair

Ruhl

Lenzen

et al. 2013

Lecture Notes in Computer Science

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“…We propose to do that using a per-pixel tracking with a Kalman filter as detailed in Section 4.2. Correcting amplitude images using a standardization step [54]. (a) and (b) show the original LR amplitude images for a dynamic scene containing a hand moving towards the camera where the intensity (amplitude) values differ significantly depending on the object distance from the camera.…”

Section: Lateral Registrationmentioning

confidence: 99%

“…Their intensity values differ significantly depending on the camera integration time and on the distance of the scene from the camera; hence, not verifying the optical flow assumption of brightness consistency. Thus, in order to guarantee an accurate registration, it is necessary to apply a standardization step similar to the one proposed in [54] prior to motion estimation, see Fig. 3.…”

Section: Lateral Registrationmentioning

confidence: 99%

Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations

Ismaeil

Aouada

Solignac

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We propose a novel approach for enhancing depth videos containing non-rigidly deforming objects. Depth sensors are capable of capturing depth maps in real-time but suffer from high noise levels and low spatial resolutions. While solutions for reconstructing 3D details in static scenes, or scenes with rigid global motions have been recently proposed, handling unconstrained non-rigid deformations in relative complex scenes remains a challenge. Our solution consists in a recursive dynamic multi-frame superresolution algorithm where the relative local 3D motions between consecutive frames are directly accounted for. We rely on the assumption that these 3D motions can be decoupled into lateral motions and radial displacements. This allows to perform a simple local per-pixel tracking where both depth measurements and deformations are dynamically optimized. The geometric smoothness is subsequently added using a multi-level L 1 minimization with a bilateral total variation regularization. The performance of this method is thoroughly evaluated on both real and synthetic data. As compared to alternative approaches, the results show a clear improvement in reconstruction accuracy and in robustness to noise, to relative large non-rigid deformations, and to topological changes. Moreover, the proposed approach, implemented on a CPU, is shown to be computationally efficient and working in real-time.

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“…Again, this is not the case due to pixel gain differences and a radial light attenuation toward the image border. Lindner and Kolb [7] propose a raw value calibration based on work in [9]. The strength and weakness of this class of methods is strongly coupled with the flow method used.…”

Section: Related Workmentioning

confidence: 99%

Time of flight motion compensation revisited

Gottfried

Nair

Meister

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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In this paper, we study motion artifacts that arise in Time-ofFlight imaging of dynamic scenes caused by the sequential nature of the raw image acquisition process used to compute the final depth image. Many methods for compensation of such errors have been proposed to date, but still lack a proper comparison. We bridge this gap by not only evaluating those methods, but also by providing implementations for all of them as a base-line to the community. By exchanging the calibration model necessary for these methods with a model closer to reality we were able to improve the results on all related methods without any loss of performance.

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Standardization of intensity-values acquired by Time-of-Flight-cameras

Abstract: The intensity-images captured by Time-of-Flight (ToF)

Cited by 15 publications

References 8 publications

A Survey on Time-of-Flight Stereo Fusion

A Survey on Time-of-Flight Stereo Fusion

Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations

Time of flight motion compensation revisited

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