MSIDA-Net: Point Cloud Semantic Segmentation via Multi-Spatial Information and Dual Adaptive Blocks

Abstract

Large-scale 3D point clouds are rich in geometric shape and scale information but they are also scattered, disordered and unevenly distributed. These characteristics lead to difficulties in learning point cloud semantic segmentations. Although many works have performed well in this task, most of them lack research on spatial information, which limits the ability to learn and understand the complex geometric structure of point cloud scenes. To this end, we propose the multispatial information and dual adaptive (MSIDA) module, which consists of a multispatial information encoding (MSI) block and dual adaptive (DA) blocks. The MSI block transforms the information of the relative position of each centre point and its neighbouring points into a cylindrical coordinate system and spherical coordinate system. Then the spatial information among the points can be re-represented and encoded. The DA blocks include a Coordinate System Attention Pooling Fusion (CSAPF) block and a Local Aggregated Feature Attention (LAFA) block. The CSAPF block weights and fuses the local features in the three coordinate systems to further learn local features, while the LAFA block weights the local aggregated features in the three coordinate systems to better understand the scene in the local region. To test the performance of the proposed method, we conducted experiments on the S3DIS, Semantic3D and SemanticKITTI datasets and compared the proposed method with other networks. The proposed method achieved 73%, 77.8% and 59.8% mean Intersection over Union (mIoU) on the S3DIS, Semantic3D and SemanticKITTI datasets, respectively.

A Category-Contrastive Guided-Graph Convolutional Network Approach for the Semantic Segmentation of Point Clouds

“…First, the efficient aggregation of rich semantic information at various scales remains difficult due to unstructured data characteristics. Although graph convolution-based methods have been explored for the semantic segmentation of point clouds in recent years [17,18,31], capturing local geometric patterns and aggregating spatial context is necessary, especially for dynamic and large scenarios [27,32,33]. Second, although semantic supervised labels effectively improve the descriptiveness of feature representation via an end-to-end deep learning architecture, most approaches update the model super-parameters to converge by only comparing their predictions with the associated semantic labels.…”

DGPolarNet: Dynamic Graph Convolution Network for LiDAR Point Cloud Semantic Segmentation on Polar BEV

“…Three-dimensional (3D) segmentation methods [4,5] based on machine learning, such as support vector machine (SVM), random forest, naïve Bayesian supervised learning, etc., usually utilize the geometrical or distribution features of point clouds to train models. The feature extraction [6,7] process of large-scale LiDAR point clouds is computationally intensive, which limits machine learning approaches for outdoor environment perception tasks. Meanwhile, because LiDAR point cloud density is high in the near field and loose in the far field, such methods have poor adaptability and expansion capabilities [8].…”

“…We utilize max-pooling and min-pooling operations to extract local features from the sampled farthest and nearest vertices, respectively. Accordingly, the output p (l+1) i ∈ V (l+1) of the FK-EdgeConv operation is denoted as Equation (7). The dataset V (l+1) is also the input of the (l+1)-th layer processed by the following FK-EdgeConv operation.…”