Projective alignment of range and parallax data

Hansard, Miles; Horaud, Radu; Amat, Michel; Lee, Seungkyu

doi:10.1109/cvpr.2011.5995533

Cited by 21 publications

(22 citation statements)

References 18 publications

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“…Recall that Fig. 1(b) highlights the resolution differences between the TOF and color images.This camera system was calibrated using the method described in [15].…”

Section: Methodsmentioning

confidence: 99%

“…The transformations allowing to project the 3D TOF points onto the 2D images are obtained via a complex sensor calibration process [15]. This introduces a localization error up to 2-3 pixels.…”

Section: B Tof Seeds and Their Refinementmentioning

confidence: 99%

“…This map can be projected onto the left and right images associated with the stereoscopic pair, using the projection matrices estimated by the calibration method described in [15]. Projecting a single 3D point (x, y, z) gathered by the TOF camera onto the rectified images provides us with a pair of corresponding points (u, v) and (u , v) in the respective images.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…A calibrated system composed of an active range sensor and a stereoscopic colorcamera pair [15], e.g., Fig. 1, allows the range data to be projected onto each one of the two images, thus providing an initial sparse set of point-to-point correspondences (seeds) between the two images.…”

Section: B Contributionsmentioning

confidence: 99%

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High-resolution depth maps based on TOF-stereo fusion

Gandhi

Čech

Horaud

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-The combination of range sensors with color cameras can be very useful for robot navigation, semantic perception, manipulation, and telepresence. Several methods of combining range-and color-data have been investigated and successfully used in various robotic applications. Most of these systems suffer from the problems of noise in the range-data and resolution mismatch between the range sensor and the color cameras, since the resolution of current range sensors is much less than the resolution of color cameras. High-resolution depth maps can be obtained using stereo matching, but this often fails to construct accurate depth maps of weakly/repetitively textured scenes, or if the scene exhibits complex self-occlusions. Range sensors provide coarse depth information regardless of presence/absence of texture. The use of a calibrated system, composed of a time-of-flight (TOF) camera and of a stereoscopic camera pair, allows data fusion thus overcoming the weaknesses of both individual sensors. We propose a novel TOF-stereo fusion method based on an efficient seed-growing algorithm which uses the TOF data projected onto the stereo image pair as an initial set of correspondences. These initial "seeds" are then propagated based on a Bayesian model which combines an image similarity score with rough depth priors computed from the low-resolution range data. The overall result is a dense and accurate depth map at the resolution of the color cameras at hand. We show that the proposed algorithm outperforms 2D image-based stereo algorithms and that the results are of higher resolution than off-the-shelf color-range sensors, e.g., Kinect. Moreover, the algorithm potentially exhibits real-time performance on a single CPU.

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“…Recall that Fig. 1(b) highlights the resolution differences between the TOF and color images.This camera system was calibrated using the method described in [15].…”

Section: Methodsmentioning

confidence: 99%

Section: B Tof Seeds and Their Refinementmentioning

confidence: 99%

Section: The Proposed Algorithmmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

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High-resolution depth maps based on TOF-stereo fusion

Gandhi

Čech

Horaud

2012

2012 IEEE International Conference on Robotics and Automation

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“…Once calibrated [13,12], this sensor provides 3D point clouds with associated color information. We gathered ten point clouds by manually moving the sensor in front of a scene, e.g., Fig.…”

Section: Experiments With Synthetic and Real Datamentioning

confidence: 99%

A Generative Model for the Joint Registration of Multiple Point Sets

Evangelidis

Kounades-Bastian

Horaud

et al. 2014

Lecture Notes in Computer Science

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144

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International audienceThis paper describes a probabilistic generative model and its associated algorithm to jointly register multiple point sets. The vast majority of state-of-the-art registration techniques select one of the sets as the ''model" and perform pairwise alignments between the other sets and this set. The main drawback of this mode of operation is that there is no guarantee that the model-set is free of noise and outliers, which contaminates the estimation of the registration parameters. Unlike previous work, the proposed method treats all the point sets on an equal footing: they are realizations of a Gaussian mixture (GMM) and the registration is cast into a clustering problem. We formally derive an EM algorithm that estimates both the GMM parameters and the rotations and translations that map each individual set onto the ''central" model. The mixture means play the role of the registered set of points while the variances provide rich information about the quality of the registration. We thoroughly validate the proposed method with challenging datasets, we compare it with several state-of-the-art methods, and we show its potential for fusing real depth data

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Time-of-Flight Cameras

Hansard

Lee

Choi

et al. 2013

SpringerBriefs in Computer Science

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173

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This chapter introduces the principles and difficulties of time-of-flight depth measurement. The depth-images that are produced by time-of-flight cameras suffer from characteristic problems, which are divided into the following two classes. Firstly there are systematic errors, such as noise and ambiguity, which are directly related to the sensor. Secondly, there are non-systematic errors, such as scattering and motion blur, which are more strongly related to the scene-content. It is shown that these errors are often quite different from those observed in ordinary color images. The case of motion blur, which is particularly problematic, is examined in detail. A practical methodology for investigating the performance of depth-cameras is presented. Time-of-flight devices are compared to structured-light systems, and the problems posed by specular and translucent materials are investigated.

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Projective alignment of range and parallax data

Cited by 21 publications

References 18 publications

High-resolution depth maps based on TOF-stereo fusion

High-resolution depth maps based on TOF-stereo fusion

A Generative Model for the Joint Registration of Multiple Point Sets

Time-of-Flight Cameras

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