Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

Recently, LiDAR (Light Detection and Ranging)-based place recognition, has been widely concerned because of its robustness to light conditions, seasonal changes, and viewpoint variations. Unlike most of existing methods which represent the whole point cloud scenes with global descriptors, we treat the LiDAR-based place recognition problem as a scene overlap prediction task and propose an end-to-end overlap prediction network, which consists of a feature learning backbone, a feature enhancement module, and an overlap prediction module. Based on the prediction result for each point, the overlapping ratios between two point clouds are computed and used to predict whether these two point clouds are at the same place. In addition, to promote the computational efficiency and reduce the model complexity, a lightweight feature learning backbone is also adopted. The experiments conducted on the KITTI Odometry dataset demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods. The lightweight method also obtains 2x inference speed with little performance degradation compared with the vanilla method.

The assessment of pavement cracks is one of the essential tasks for road maintenance. This paper presents a novel framework, called ITVCrack, for automated crack extraction based on iterative tensor voting (ITV), from high-density point clouds collected by a mobile laser scanning system. The proposed ITVCrack comprises the following: 1) the preprocessing involving the separation of road points from nonroad points using vehicle trajectory data; 2) the generation of the georeferenced feature (GRF) image from the road points; and 3) the ITV-based crack extraction from the noisy GRF image, followed by an accurate delineation of the curvilinear cracks. Qualitatively, the method is applicable for pavement cracks with low contrast, low signal-to-noise ratio, and bad continuity. Besides the application to GRF images, the proposed framework demonstrates much better crack extraction performance when quantitatively compared to existing methods on synthetic data and pavement images.

This letter presents a novel semisupervised method for addressing a domain adaptation problem in the classification of hyperspectral data. To overcome the influence of distribution bias between the source and target domains, we introduce the domain transfer multiple-kernel learning to simultaneously minimize the maximum mean discrepancy criterion and the structural risk functional of support vector machines. Then, the pairwise binary classifiers are merged as the multiclass classifier for solving the classification problem in hyperspectral data. Both bias and nonbias sampling strategies are introduced to evaluate the robustness of the proposed method against the spectral distribution bias. The results obtained from real data sets show that the proposed method can achieve higher classification accuracy even with cross-domain distribution bias and provide robust solutions with different labeled and unlabeled data sizes.

A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition. Because of the irregular distribution property of point clouds and the massive data volume, point cloud classification directly from multispectral LiDAR data is still challengeable and questionable. In this paper, a point-wise multispectral LiDAR point cloud classification architecture termed as SE-PointNet++ is proposed via integrating a Squeeze-and-Excitation (SE) block with an improved PointNet++ semantic segmentation network. PointNet++ extracts local features from unevenly sampled points and represents local geometrical relationships among the points through multi-scale grouping. The SE block is embedded into PointNet++ to strengthen important channels to increase feature saliency for better point cloud classification. Our SE-PointNet++ architecture has been evaluated on the Titan multispectral LiDAR test datasets and achieved an overall accuracy, a mean Intersection over Union (mIoU), an F₁-score, and a Kappa coefficient of 91.16%, 60.15%, 73.14%, and 0.86, respectively. Comparative studies with five established deep learning models confirmed that our proposed SE-PointNet++ achieves promising performance in multispectral LiDAR point cloud classification tasks.

The accurate extraction of roads is a prerequisite for the automatic extraction of other road features. This letter describes a method for detecting road boundaries from mobile laser scanning (MLS) point clouds in an urban environment. The key idea of our method is directly constructing a saliency map on 3-D unorganized point clouds to extract road boundaries. The method consists of four major steps, i.e., road partition with the assistance of the vehicle trajectory, salient map construction and salient points extraction, curb detection and curb lowest points extraction, and road boundaries fitting. The performance of the proposed method is evaluated on the point clouds of an urban scene collected by a RIEGL VMX-450 MLS system. The completeness, correctness, and quality of the extracted road boundaries are 95.41%, 99.35%, and 94.81%, respectively. Experimental results demonstrate that our method is feasible for detecting road boundaries in MLS point clouds.