Pixel-Wise Weighted Region-Based 3D Object Tracking Using Contour Constraints

Huang, Hong; Zhong, Fan; Qin, Xueying

doi:10.1109/tvcg.2021.3085197

Cited by 12 publications

(18 citation statements)

References 31 publications

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“…Recent progress is mainly achieved by the region-based methods [7,18,19,29]. PWP3D [16] is the first real-time region-based method.…”

Section: Related Workmentioning

confidence: 99%

“…For an input image, we first compute a foreground probability map p(x) based on color histograms. The approach is widely used in previous region-based methods [7,18,22]. As shown in Figure 2(c), the probability map is in fact a soft segmentation of the object, based on which the influence of background clutters and interior contours can be suspended effectively.…”

Section: Contour Correspondences Based On Probability Gradientsmentioning

confidence: 99%

“…Considering large object displacements, we estimate p(x) based on the global color histograms. Note that although local color probabilities have been shown to handle complex and indistinctive color distributions better [7,22], the local window size is usually hard to be determined for the case of large displacement.…”

Section: Contour Correspondences Based On Probability Gradientsmentioning

confidence: 99%

“…Eq. ( 7) is a nonlinear weighted least square problem that can be solved similarly as in previous methods [7,22]. Given the Jacobian J of F (ξ, i), the pose update of each iteration can be computed as…”

Section: Pose Optimizationmentioning

confidence: 99%

“…The coarse-to-fine search is commonly adopted in previous tracking methods for handling large displacements. For 3D tracking, it can be implemented by image pyramids [7,22] or by varying the length of search lines [18]. However, note that since the 3D rotation is independent of the object scale in image space, coarse-to-fine search in image space would take little effect on the rotation components.…”

Section: Introductionmentioning

confidence: 99%

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Large-displacement 3D Object Tracking with Hybrid Non-local Optimization

Tian¹,

Lin²,

Fan³

et al. 2022

Preprint

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Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements. In this paper we propose a fast and effective non-local 3D tracking method. Based on the observation that erroneous local minimum are mostly due to the out-of-plane rotation, we propose a hybrid approach combining non-local and local optimizations for different parameters, resulting in efficient non-local search in the 6D pose space. In addition, a precomputed robust contour-based tracking method is proposed for the pose optimization. By using long search lines with multiple candidate correspondences, it can adapt to different frame displacements without the need of coarse-to-fine search. After the pre-computation, pose updates can be conducted very fast, enabling the non-local optimization to run in real time. Our method outperforms all previous methods for both small and large displacements. For large displacements, the accuracy is greatly improved (81.7% v.s. 19.4%). At the same time, real-time speed (>50fps) can be achieved with only CPU. The source code is available at https://github.com/cvbubbles/nonlocal-3dtracking.

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“…Recent progress is mainly achieved by the region-based methods [7,18,19,29]. PWP3D [16] is the first real-time region-based method.…”

Section: Related Workmentioning

confidence: 99%

Section: Contour Correspondences Based On Probability Gradientsmentioning

confidence: 99%

Section: Contour Correspondences Based On Probability Gradientsmentioning

confidence: 99%

Section: Pose Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large-displacement 3D Object Tracking with Hybrid Non-local Optimization

Tian¹,

Lin²,

Fan³

et al. 2022

Preprint

Self Cite

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A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

Stoiber

Pfanne

Strobl

et al. 2021

Lecture Notes in Computer Science

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In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multimodality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, which uses geometry, to additionally consider surface appearance. In general, object surfaces contain local characteristics from text, graphics, and patterns, as well as global differences from distinct materials and colors. To incorporate this visual information, two modalities are developed. For local characteristics, keypoint features are used to minimize distances between points from keyframes and the current image. For global differences, a novel region approach is developed that considers multiple regions on the object surface. In addition, it allows the modeling of external geometries. Experiments on the YCB-Video and OPT datasets demonstrate that our approach ICG+ performs best on both datasets, outperforming both conventional and deep learningbased methods. At the same time, the algorithm is highly efficient and runs at more than 300 Hz. The source code of our tracker is publicly available.

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