Point Cloud Registration With Object-Centric Alignment

Žagar, Bare Luka; Yurtsever, Ekim; Peters, Arne; Knoll, Alois

doi:10.1109/access.2022.3191352

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…In addition, Žagar et al ( 2022 ) proposed a point cloud registration method centered on target objects in view of the impact of non-overlapping regions in feature extraction by global feature-based registration methods. First, the most distant point sampling (FPS) is used to stratify the input point cloud to extract the object of interest.…”

Section: Incomplete Point Cloud Registrationmentioning

confidence: 99%

A review of rigid point cloud registration based on deep learning

Chen,

Feng,

et al. 2024

Front. Neurorobot.

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With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.

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Section: Incomplete Point Cloud Registrationmentioning

confidence: 99%

A review of rigid point cloud registration based on deep learning

Chen,

Feng,

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

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“…However, the accuracy of adaptive alignment using only time-series point cloud data is often insufficient for phenotype extraction. Therefore, the advantages of RGB cameras and LiDAR can be combined to monitor crop phenotypes and improve the accuracy of the corresponding phenotypic parameters [ 34 , 35 ]. The HTP platform can be used to simultaneously acquire RGB images of the field and 3D LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

Wang

Fan

et al. 2023

Plant Phenomics

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The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements ( R 2 = 0.98), and the accuracy was higher than only using one source point cloud data ( R 2 = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.

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“…Like other optimization-based PCR methods, our previous methods were highly sensitive to initialization and failed to handle large transformations well. In real-world applications, there are often large transformations and noise between point clouds, so it is a challenge to make the point cloud registration algorithm converge even in the case of noise and large transformations [13,14]. In recent years, several deep learning architectures utilizing point clouds have demonstrated remarkable performance in various 3D vision tasks, including classification, segmentation, and detection.…”

Section: Introductionmentioning

confidence: 99%

Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence

Zhang,

Lyu,

Min

et al. 2023

Remote Sensing

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Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method for accurately estimating point correspondences and handling large transformations using limited prior datasets. Firstly, a modified autoencoder is introduced as the feature extraction module to extract the distinctive and robust features for the downstream registration task. Unlike optimization-based pairwise PCR strategies, the proposed method treats two point clouds as two implementations of a Gaussian mixture model (GMM), which we call latent GMM. Based on the above assumption, two point clouds can be regarded as two probability distributions. Hence, the PCR of two point clouds can be approached by minimizing the KL divergence between these two probability distributions. Then, the correspondence between the point clouds and the latent GMM components is estimated using the augmented regression network. Finally, the parameters of GMM can be updated by the correspondence and the transformation matrix can be computed by employing the weighted singular value decomposition (SVD) method. Extensive experiments conducted on both synthetic and real-world data validate the superior performance of the proposed method compared to state-of-the-art registration methods. These experiments also highlight the method’s superiority in terms of accuracy, robustness, and generalization.

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Point Cloud Registration With Object-Centric Alignment

Cited by 7 publications

References 44 publications

A review of rigid point cloud registration based on deep learning

A review of rigid point cloud registration based on deep learning

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence

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