Range Flow Estimation

Spies, Hagen; Jähne, Bernd; Barron, John A.

doi:10.1006/cviu.2002.0970

Cited by 61 publications

(62 citation statements)

References 24 publications

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“…Optical flow calculation for range images, also known as range flow (Spies et al, 2002), is expected to have the same benefit as optical flow from this approach. Depth from stereo (Beardsley et al, 1996) relies on extracting information from multiple images, and requires piecewise smoothness at object boundaries.…”

Section: Discussionmentioning

confidence: 99%

Coarse to over-fine optical flow estimation

Amiaz

Lubetzky

Kiryati

2007

Pattern Recognition

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We present a readily applicable way to go beyond the accuracy limits of current optical flow estimators. Modern optical flow algorithms employ the coarse to fine approach. We suggest to upgrade this class of algorithms, by adding over-fine interpolated levels to the pyramid. Theoretical analysis of the coarse to over-fine approach explains its advantages in handling flow-field discontinuities and simulations show its benefit for sub-pixel motion. By applying the suggested technique to various multiscale optical flow algorithms, we reduced the estimation error by 10%-30% on the common test sequences. Using the coarse to over-fine technique, we obtain optical flow estimation results that are currently the best for benchmark sequences.

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Section: Discussionmentioning

confidence: 99%

Coarse to over-fine optical flow estimation

Amiaz

Lubetzky

Kiryati

2007

Pattern Recognition

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“…The range flow constraint has been first proposed in [45], and later appeared in other references [46], [47], [48], [49]. This constraint is usually used in a variational framework to estimate the range flow.…”

Section: Range Flow Estimationmentioning

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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“…Spies et al [15] introduce a depth analog to brightness constancy that they called the range flow constraint. Other notable scene flow techniques are that of Huguet and Devernay [16], who add explicit occlusion reasoning for pixels, and that of Vogel et al [17], who add a local rigidity term.…”

Section: Related Workmentioning

confidence: 99%

RGB-D flow: Dense 3-D motion estimation using color and depth

Herbst

Ren

Fox

2013

2013 IEEE International Conference on Robotics and Automation

144

114

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Abstract-3-D motion estimation is a fundamental problem that has far-reaching implications in robotics. A scene flow formulation is attractive as it makes no assumptions about scene complexity, object rigidity, or camera motion. RGB-D cameras provide new information useful for computing dense 3-D flow in challenging scenes. In this work we show how to generalize two-frame variational 2-D flow algorithms to 3-D. We show that scene flow can be reliably computed using RGB-D data, overcoming depth noise and outperforming previous results on a variety of scenes. We apply dense 3-D flow to rigid motion segmentation.

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Range Flow Estimation

Cited by 61 publications

References 24 publications

Coarse to over-fine optical flow estimation

Coarse to over-fine optical flow estimation

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

RGB-D flow: Dense 3-D motion estimation using color and depth

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