3D face dense reconstruction based on sparse points using probabilistic principal component analysis

Xie, Xiaoxiao; Wang, Xingce

doi:10.1007/s11042-021-11707-0

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…The current popular 3D vision solution is to estimate the pose of the target by point cloud registration. Common point cloud registration algorithms include the Normal Distributions Transform (NDT) [ 19 ] algorithm, Principal Component Analysis (PCA) algorithm [ 20 ], Iterative Closest Point (ICP) algorithm [ 21 ], and many other improved algorithms. The principle of the traditional ICP algorithm is popular and easy to understand, and the registration effect is remarkable; thus, it is widely used in point cloud registration.…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

Shao

Fan

Zhu

et al. 2023

Sensors

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Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%