MVSalNet: Multi-view Augmentation for RGB-D Salient Object Detection

Zhou, Jianli; Wang, Lijun; Lu, Huchuan; Huang, Kaining; Shi, Xinchu; Liu, Bocong

doi:10.1007/978-3-031-19818-2_16

Cited by 11 publications

(1 citation statement)

References 46 publications

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“…To solve this issue, Zhou et al (2022) suggest a new framework. Huang et al (2022) provide a method for reconstructing a hand-held item from a monocular video.…”

Section: Related Workmentioning

confidence: 99%

FADM-SLAM: a fast and accurate dynamic intelligent motion SLAM for autonomous robot exploration involving movable objects

Islam

Ibrahim

Chin

et al. 2023

RIA

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Purpose Many popular simultaneous localization and mapping (SLAM) techniques have low accuracy, especially when localizing environments containing dynamically moving objects since their presence can potentially cause inaccurate data associations. To address this issue, the proposed FADM-SLAM system aims to improve the accuracy of SLAM techniques in environments containing dynamically moving objects. It uses a pipeline of feature-based approaches accompanied by sparse optical flow and multi-view geometry as constraints to achieve this goal. Design/methodology/approach FADM-SLAM, which works with monocular, stereo and RGB-D sensors, combines an instance segmentation network incorporating an intelligent motion detection strategy (iM) with an optical flow technique to improve location accuracy. The proposed AS-SLAM system comprises four principal modules, which are the optical flow mask and iM, the ego motion estimation, dynamic point detection and the feature-based extraction framework. Findings Experiment results using the publicly available RGBD-Bonn data set indicate that FADM-SLAM outperforms established visual SLAM systems in highly dynamic conditions. Originality/value In summary, the first module generates the indication of dynamic objects by using the optical flow and iM with geometric-wise segmentation, which is then used by the second module to compute the starting point of a posture. The third module, meanwhile, first searches for the dynamic feature points in the environment, and second, eliminates them from further processing. An algorithm based on epipolar constraints is implemented to do this. In this way, only the static feature points are retained, which are then fed to the fourth module for extracting important features.

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“…To solve this issue, Zhou et al (2022) suggest a new framework. Huang et al (2022) provide a method for reconstructing a hand-held item from a monocular video.…”

Section: Related Workmentioning

confidence: 99%