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.

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.