Prior-Free Dependent Motion Segmentation Using Helmholtz-Hodge Decomposition Based Object-Motion Oriented Map

Zhang, Cui-Cui; Liu, Zhilei

doi:10.1007/s11390-017-1741-z

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Previous methods for motion segmentation are mainly based on subspace factorization techniques [24,25], statistical modeling, and sampling [26][27][28], epipolar or trilinear constraints [29,30], object or scene flow [14,17,31,32], energy minimization [20,33,34], and deep learning based instance-level detection [7,10,12,21,35] (i.e., tracking-by-detection). Our strategy for segmenting multiple instances differs from previous approaches.…”

Section: Multibody Motion Segmentationmentioning

confidence: 99%

ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

et al. 2021

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We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

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Section: Multibody Motion Segmentationmentioning

confidence: 99%

ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

et al. 2021

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show abstract

“…Multibody motion segmentation. Previous methods for motion segmentation are mainly based on subspace factorization techniques [9,28], statistical modeling and sampling [15,3,45], epipolar/trilinear constraints [42,41], object/scene flow [27,2,20,43], energy minimization [47,23,21], and deep learning based instance-level detection [19,12,4,14] (i.e. tracking-by-detection).…”

Section: Related Workmentioning

confidence: 99%

ClusterSLAM: A SLAM Backend for Simultaneous Rigid Body Clustering and Motion Estimation

Huang

Yang

Zhao

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, the dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions among the landmarks extracted from the same rigid body for clustering and estimating static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix upon landmarks, and uses agglomerative clustering for distinguishing those rigid bodies. Accompanied by a decoupled factor graph optimization for revising their shape and trajectory, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneous tracking of egomotion and multiple objects.

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Maximizing a deep submodular function optimization with a weighted MAX-SAT problem for trajectory clustering and motion segmentation

Chandriah

Naraganahalli²

2021

Appl Intell

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Prior-Free Dependent Motion Segmentation Using Helmholtz-Hodge Decomposition Based Object-Motion Oriented Map

Cited by 7 publications

References 34 publications

ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

ClusterSLAM: A SLAM Backend for Simultaneous Rigid Body Clustering and Motion Estimation

Maximizing a deep submodular function optimization with a weighted MAX-SAT problem for trajectory clustering and motion segmentation

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