Efficient Dense Rigid-Body Motion Segmentation and Estimation in RGB-D Video

Stückler, Jörg; Behnke, Sven

doi:10.1007/s11263-014-0796-3

Cited by 26 publications

(28 citation statements)

References 39 publications

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“…Rotations are typically harder to handle as they result in challenging self‐occlusions. We recorded one_chair, two_chairs and statue ourselves; bonn_chair, bonn_can1 and bonn_can2 are from data shared by Stückler and Behnke [SB15], and ambush5 is from the Sintel dataset [BWSB12].…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…We show an example ground‐truth segmentation for the one_chair sequence in Figure . While Stückler and Behnke [SB15] already provide ground‐truth segmentations on a few frames, we use our annotations instead for consistency. The ternary masks with optional positive pixels for each label allow us to obtain a more fine‐grained evaluation, compared to masking ambiguous pixels as ‘do not care’ for all labels.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…Results on a sub‐sequence of the bonn_can1 and bonn_can2 scene of Stückler and Behnke [SB15]. From top to bottom: visualization of the ground‐truth, results obtained by IROS16 [KLAK16], EG16 [YLX*16] and our method.…”

Section: Discussionmentioning

confidence: 99%

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Temporally Consistent Motion Segmentation From RGB‐D Video

Bertholet

Ichim

Zwicker

2018

Computer Graphics Forum

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Temporally consistent motion segmentation from RGB‐D videos is challenging because of the limitations of current RGB‐D sensors. We formulate segmentation as a motion assignment problem, where a motion is a sequence of rigid transformations through all frames of the input. We capture the quality of each potential assignment by defining an appropriate energy function that accounts for occlusions and a sensor‐specific noise model. To make energy minimization tractable, we work with a discrete set instead of the continuous, high dimensional space of motions, where the discrete motion set provides an upper bound for the original energy. We repeatedly minimize our energy, and in each step extend and refine the motion set to further lower the bound. A quantitative comparison to the current state of the art demonstrates the benefits of our approach in difficult scenarios.

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Section: Implementation and Resultsmentioning

confidence: 99%

Section: Implementation and Resultsmentioning

confidence: 99%

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Temporally Consistent Motion Segmentation From RGB‐D Video

Bertholet

Ichim

Zwicker

2018

Computer Graphics Forum

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“…An expectation‐maximization (EM) framework is introduced in [SB15] for motion segmentation of RGB‐D sequences. Motion segments and their 3D rigid‐body transformations are formulated as an EM problem, thus it determines the number of rigid parts, their 3D rigid‐ body motion, and the image regions that map these parts.…”

Section: Related Workmentioning

confidence: 99%

Mobility Fitting using 4D RANSAC

Wan

et al. 2016

Computer Graphics Forum

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Capturing the dynamics of articulated models is becoming increasingly important. Dynamics, better than geometry, encode the functional information of articulated objects such as humans, robots and mechanics. Acquired dynamic data is noisy, sparse, and temporarily incoherent. The latter property is especially prominent for analysis of dynamics. Thus, processing scanned dynamic data is typically an ill‐posed problem. We present an algorithm that robustly computes the joints representing the dynamics of a scanned articulated object. Our key idea is to by‐pass the reconstruction of the underlying surface geometry and directly solve for motion joints. To cope with the often‐times extremely incoherent scans, we propose a space‐time fitting‐and‐voting approach in the spirit of RANSAC. We assume a restricted set of articulated motions defined by a set of joints which we fit to the 4D dynamic data and measure their fitting quality. Thus, we repeatedly select random subsets and fit with joints, searching for an optimal candidate set of mobility parameters. Without having to reconstruct surfaces as intermediate means, our approach gains the advantage of being robust and efficient. Results demonstrate the ability to reconstruct dynamics of various articulated objects consisting of a wide range of complex and compound motions.

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“…They require, however, a plane fitting step to make the method robust. Closely related to our method is the approach by Stueckler and Behnke [19]. They jointly estimate motion and segmentation of rigid bodies in an expectationmaximization framework in RGB-D video.…”

Section: Related Workmentioning

confidence: 99%

Motion Cooperation: Smooth Piece-wise Rigid Scene Flow from RGB-D Images

Jaimez

Souiai

Stückler

et al. 2015

2015 International Conference on 3D Vision

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We propose a novel joint registration and segmentation approach to estimate scene flow from RGB-D images. Instead of assuming the scene to be composed of a number of independent rigidly-moving parts, we use non-binary labels to capture non-rigid deformations at transitions between the rigid parts of the scene. Thus, the velocity of any point can be computed as a linear combination (interpolation) of the estimated rigid motions, which provides better results than traditional sharp piecewise segmentations. Within a variational framework, the smooth segments of the scene and their corresponding rigid velocities are alternately refined until convergence. A K-means-based segmentation is employed as an initialization, and the number of regions is subsequently adapted during the optimization process to capture any arbitrary number of independently moving objects. We evaluate our approach with both synthetic and real RGB-D images that contain varied and large motions. The experiments show that our method estimates the scene flow more accurately than the most recent works in the field, and at the same time provides a meaningful segmentation of the scene based on 3D motion.

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Efficient Dense Rigid-Body Motion Segmentation and Estimation in RGB-D Video

Cited by 26 publications

References 39 publications

Temporally Consistent Motion Segmentation From RGB‐D Video

Temporally Consistent Motion Segmentation From RGB‐D Video

Mobility Fitting using 4D RANSAC

Motion Cooperation: Smooth Piece-wise Rigid Scene Flow from RGB-D Images

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