Laser scanning technology plays an important role in forest inventory, as it enables accurate 3D information capturing in a fast and environmentally-friendly manner. The goal of this study is to develop methods for detecting and discriminating individual trees from TLS point clouds of five plots in a boreal coniferous forest. The proposed hierarchical minimum cut method adopts the detected trunk points that are recognized according to pole like shape segmentation as foreground seed points and other points as background seed points, respectively. It constructs the undirected weighted graph of the foreground and background seed points to deduce a cost function for tree crown point segmentation with the decreasing ranking of tree trunk heights. The intermediate results lead to global optimization segmentation of individual trees in a hierarchical order. Finally, the structure metrics of the detected individual trees are calculated and checked with field observations. Plots with different attributes were selected to verify the proposed method, and the experimental studies show that the proposed method is efficient and robust for extracting individual trees from TLS point clouds in terms of the recall of 90.42%.

Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points.

Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.

Point cloud registration is a necessary prerequisite for conducting precise, large-scale forest surveys and management. This paper focuses on providing a systematic overview and summary of the work on forest point cloud registration over the past 20 years. The developmental process of forest point cloud registration methods, spanning from the early reliance on manual markers to the subsequent evolution towards automatic registration based on feature matching, and then to the advanced technology based on deep learning were reviewed. Furthermore, the paper offered detailed discussions on the registration between different point cloud platforms: ground platforms, between ground platforms and aerial platforms, and between aerial platforms. Additionally, the paper delved into mainstream datasets and evaluation metrics in the domain of forest point cloud registration. Finally, the paper summarized the current state of research in this area, highlighted challenges, and provided future research outlooks. This review aims to provide researchers with a comprehensive understanding of forest point cloud registration, and to promote the advancement of point cloud technology, hopefully inspiring further applications in the field.