Simulated dataset collection method of dynamic quality of services (QoS) metrics

Hasnain, Muhammad; Pasha, Muhammad Fermi; Ghani, Imran; Jeong, Seung Ryul

doi:10.1007/s41870-021-00647-5

Cited by 4 publications

(1 citation statement)

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“…Active sensors should be used for the acquisition of accurate and complete surface information, but the process usually takes several hours, for instance, when acquiring data on moving dynamic objects in a field of view [7]. The existing mature technique COLMAP [8] can accurately estimate the camera's pose and thus obtain complete surface information but requires precise and non-overlapping camera coordinate positions; therefore, the application of this operational technique is limited.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Method for PatchMatch Multi-View Stereo with Sparse Points

Zhan¹,

Cao²,

Jiang³

et al. 2022

Photonics

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Recently, the deep-learning-based PatchMatch method has been rapidly developed in 3D reconstruction, based on which boundary regions are filled with other parts that most closely match edge parts, but limited PatchMatch data hinder the generalization of the method to unknown settings. If various large-scale PatchMatch datasets are generated, the process would require considerable time and resources when performing neighborhood point-matching calculations using random iterative algorithms. To solve this issue, we first propose a new, sparse, semi-supervised stereo-matching framework called SGT-PatchMatchNet, which can reconstruct reliable 3D structures with a small number of 3D points using the ground truth of surface frame values. Secondly, in order to solve the problem of the luminosity inconsistency of some pixels in other views, a photometric similar-point loss function is proposed to improve the performance of 3D reconstruction, which causes the neighborhood information to project the depth value of the predicted depth to meet the same 3D coordinates. Finally, in order to solve the problem of the edge blurring of the depth map obtained using the network model, we propose a robust-point consistency loss function to improve the integrity and robustness of the occlusion and edge areas. The experimental results show that the proposed method not only has good visual effects and performance indicators but can also effectively reduce the amount of computation and improve the calculation time.

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Section: Introductionmentioning

confidence: 99%