To accurately estimate full-degree-of-freedom (DOF) model parameters of the 3D point cloud acquisition device, a calibration method by using a calibrator of a simple space ball is proposed. The method can achieve full-DOF (DOF) estimation without additional hardware and step-by-step calculations. Firstly, a measurement model is established according to the rotation characteristics of the 3D point cloud acquisition device. Secondly, with the spherical constraints of the sphere, a nonlinear optimization model of the 3D point cloud acquisition device is established by adopting the bidirectional kernel meanp-power error (BiK) loss function which is robust to the measurement noise and outliers. Finally, the successful history-based differential evolution parameter adaptation (SHADE) algorithm and the Levenberg-Marquardt (LM) algorithm are combined to solve the nonlinear optimization model, and the full-DOF model parameters of the 3D point cloud acquisition device can be estimated. Experimental results demonstrates that the proposed method can accurately estimate the full-DOF model parameters of the 3D point cloud acquisition device. More encouragingly, the effect of measurement noise and outliers can be significantly suppressed.

Multi-view point cloud registration remains a significant challenge in 3D computer vision due to the sensitivity to outliers. In this paper, we propose a clustering multi-view point cloud registration algorithm based on kernel mean p-power error (KMPE) loss function. Firstly, the 3D point clouds are clustered by using the K-means algorithm, where the centroids of the clusters are regarded as the model point cloud for multi-view point cloud registration. Secondly, the model point cloud is used to estimate the rigid transformation of each point cloud sequentially. Considering that the KMPE loss function can efficiently suppress outliers, a robust point cloud rigid registration optimization model based on the KMPE loss function is established. The Levenberg-Marquardt (LM) algorithm is adopted to optimize the optimization model to obtain the optimal rigid transformation between each point cloud and the model point cloud. Finally, the clustering and KMPE-based rigid transformation estimation are iteratively and alternatively applied to all point clouds to realize multi-view point cloud registration. Experimental results demonstrate that the proposed algorithm can effectively suppress the influence of outliers on the multi-view point cloud registration of, and can achieve high accuracy of multi-view point cloud registration.

Intended to boost the simplification precision of 3D point clouds and alleviate the cavity issue caused by overemphasis on feature components, this study puts forward a 3D point cloud simplification method that combines partition strategy and information, with the simplification rate being controllable. Using the partitioning strategy, the original point cloud is divided into three regions: edge points, feature points, and non-feature points. and the three regions are simplified respectively, and the points with a large amount of information are retained by calculating and updating the mutual information amount of each point in each region. The results demonstrate that the proposed method can effectively enhance the accuracy of simplified point clouds across various types of datasets, and maintain good simplification accuracy even when the simplification rate is large.

Graph convolution neural networks (GCNNs) have shown great promise in handling point cloud due to their adeptness at extracting topological information. However, a significant limitation of current GNN approaches is the substantial time required for constructing and learning the graph structures inherent in the point cloud. To address this challenge, we present a novel graph convolution-based network designed for the efficient processing of 3D point cloud. Based on dynamic GCNN (DGCNN) and PointNet++, our network integrates graph convolution with multi-scale point and edge features for enhanced neighborhood geometric information extraction. We introduce an innovative lightweight edge convolution technique that notably reduces computational burden while optimizing processing efficiency. In addition, the network also employs a downsampling strategy within a GCNN framework and introduces a directed graph-based geometric feature extractor to robustly capture local geometries. Empirically, our model demonstrates state-of-the-art performance across three benchmark datasets, markedly improving both accuracy and processing speed in point cloud analysis. Notably, our model achieves a groundbreaking 90% overall accuracy on the ScanObjectNN dataset.

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7\%, 81.4\%, and 69.1\% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available upon publication.

Earth’s surface is covered by thin-cloud throughout the year, which greatly limits the application of remote sensing (RS) images obtained at a high cost. Currently, deep-learning technology has received widespread attention in the field of thin-cloud removal from RS images. The parameter training process of the deep network in this letter is divided into two stages according to the frequency domain feature of training samples. In the first stage, the loss between the thin-cloud removal image and the corresponding clear image is minimized by the full-frequency domain training. In the second stage, the parameters of the first stage are frozen, and the high-frequency features of the thin-cloud image are integrated into the Decoder module for network training. Furthermore, the corresponding attention module of the dual-tree wavelet is designed. For better training in the second stage, the high-frequency loss function based on three-level wavelet transform is designed. Compared with the highest average values of peak signal to noise ratio (PSNR) and structural similarity (SSIM) of traditional methods, FDT-Net achieves an improvement by up to 33.4108% and 7.5184%, respectively. In quantitative experiments, compared with the highest average values of PSNR and SSIM among deep-learning methods, FDT-Net has an improvement by 2.1505% and 0.9123%, respectively. Therefore, the proposed deep-learning network FDT-Net can significantly extend the temporal and spatial range of RS image application under thin-cloud weather conditions. The code of model is available at https://github.com/chonghaozhan/FDT-Net.