STAR3D: Simultaneous Tracking and Reconstruction of 3D Objects Using RGB-D Data

Ren, Carl Yuheng; Prisacariu, Victor Adrian; Murray, David; Reid, Ian

doi:10.1109/iccv.2013.197

Cited by 46 publications

(26 citation statements)

References 7 publications

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“…13a we track two interacting hands (fixed hand articulation pose). Even though the hand models do not fit the observation perfectly-indeed they are models of hands from a different person obtained using the algorithm (Ren et al 2013)-the tracker still recovers the poses of both by finding the local minimum that best explains the colour and depth observations.…”

Section: Qualitative Experimentsmentioning

confidence: 99%

“…On the left we show the depth image overlaid with the tracking result and on the right we visualise a virtual sword with the corresponding 3D pose overlaid on the RGB image recovered by evolving this embedding function. A faster approach has been linked with a 3D reconstruction stage, both without depth data by Prisacariu et al ( , 2013 and with depth by Ren et al (2013). The SDF was used by Ren and Reid (2012) to formulate different embedding functions for robust real-time 3D tracking of rigid objects using only depth data, an approach extended by Ren et al (2013) to leverage RGB data in addition.…”

Section: Related Workmentioning

confidence: 99%

“…A faster approach has been linked with a 3D reconstruction stage, both without depth data by Prisacariu et al ( , 2013 and with depth by Ren et al (2013). The SDF was used by Ren and Reid (2012) to formulate different embedding functions for robust real-time 3D tracking of rigid objects using only depth data, an approach extended by Ren et al (2013) to leverage RGB data in addition. A similar idea is described by Sturm et al (2013), who use the gradient of the SDF directly to track camera pose.…”

Section: Related Workmentioning

confidence: 99%

“…We first present a revision of our earlier 3D object tracking method using RGB-D imagery (Ren et al 2013). Like many current 3D trackers, this was developed for single object tracking only.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

et al. 2017

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We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depthcolour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection.

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Section: Qualitative Experimentsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

et al. 2017

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“…The descriptors matching could also be performed on GPU, or leverage the approximate k-nearestneighbours methods based on KD-Trees such a FLANN [36]. Similarly, an efficient GPU implementation of SLIC have also been proposed [37].…”

Section: Complexity Studymentioning

confidence: 99%

Region-Based Prediction for Image Compression in the Cloud

Bégaint

Thoreau

Guillotel

et al. 2018

IEEE Trans. on Image Process.

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Multimodal obstacle detection in unstructured environments with conditional random fields

Kragh

Underwood

2019

Journal of Field Robotics

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Reliable obstacle detection and classification in rough and unstructured terrain such as agricultural fields or orchards remains a challenging problem. These environments involve large variations in both geometry and appearance, challenging perception systems that rely on only a single sensor modality. Geometrically, tall grass, fallen leaves, or terrain roughness can mistakenly be perceived as nontraversable or might even obscure actual obstacles. Likewise, traversable grass or dirt roads and obstacles such as trees and bushes might be visually ambiguous. In this paper, we combine appearance‐ and geometry‐based detection methods by probabilistically fusing lidar and camera sensing with semantic segmentation using a conditional random field. We apply a state‐of‐the‐art multimodal fusion algorithm from the scene analysis domain and adjust it for obstacle detection in agriculture with moving ground vehicles. This involves explicitly handling sparse point cloud data and exploiting both spatial, temporal, and multimodal links between corresponding 2D and 3D regions. The proposed method was evaluated on a diverse data set, comprising a dairy paddock and different orchards gathered with a perception research robot in Australia. Results showed that for a two‐class classification problem (ground and nonground), only the camera leveraged from information provided by the other modality with an increase in the mean classification score of 0.5%. However, as more classes were introduced (ground, sky, vegetation, and object), both modalities complemented each other with improvements of 1.4% in 2D and 7.9% in 3D. Finally, introducing temporal links between successive frames resulted in improvements of 0.2% in 2D and 1.5% in 3D.

show abstract

STAR3D: Simultaneous Tracking and Reconstruction of 3D Objects Using RGB-D Data

Cited by 46 publications

References 7 publications

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

Region-Based Prediction for Image Compression in the Cloud

Multimodal obstacle detection in unstructured environments with conditional random fields

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