Three Dimensional Laser Point Cloud Slicing Method for Calculating Irregular Volume

Volume parameter is the basic content of a spatial body object morphology analysis.However,the challenge lies in the volume calculation of irregular objects.The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanning(3DLS).In this method,a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS.A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained.Subsequently,the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon.The point cloud slice is also calculated.Finally,the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap.Thus,the total volume of the scanned spatial object can be calculated by summing up the individual volumes.According to the results and analysis of the calculated examples,the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct,concise in process,reliable in results,efficient in calculation methods,and controllable on accuracy.This method comes as a good solution to the volume calculation of irregular objects.

Triangular Mesh Model Reconstruction from Scan Point Clouds Based on Template

For mesh reconstruction problems of point cloud models which have similar topological structure, a rapid and efficient method is presented to reconstruct triangular mesh surface. Based on projections of point cloud slicing that correspond to template sectional curves, the method constructs topological relevant information among discrete points, which makes unorganized points ordering and builds up optimal approximated B-spline curve, resamples every curve according to its curvature distribution and performes triangular mesh division on it. Finally, surface reconstruction is achieved. The experimental results demonstrate that the surface reconstruction is done as the point cloud hole is filled simultaneously.

Accuracy assessment and error analysis for diameter at breast height measurement of trees obtained using a novel backpack LiDAR system

Background: The LiBackpack is a recently developed backpack light detection and ranging(LiDAR) system that combines the flexibility of human walking with the nearby measurement in all directions to provide a novel and efficient approach to LiDAR remote sensing, especially useful for forest structure inventory. However, the measurement accuracy and error sources have not been systematically explored for this system.Method: In this study, we used the LiBackpack D-50 system to measure the diameter at breast height(DBH) for a Pinus sylvestris tree population in the Saihanba National Forest Park of China, and estimated the accuracy of LiBackpack measurements of DBH based on comparisons with manually measured DBH values in the field. We determined the optimal vertical slice thickness of the point cloud sample for achieving the most stable and accurate LiBackpack measurements of DBH for this tree species, and explored the effects of different factors on the measurement error.Result: 1) A vertical thickness of 30 cm for the point cloud sample slice provided the highest fitting accuracy(adjusted R2= 0.89, Root Mean Squared Error(RMSE) = 20.85 mm);2) the point cloud density had a significant negative, logarithmic relationship with measurement error of DBH and it explained 35.1% of the measurement error;3) the LiBackpack measurements of DBH were generally smaller than the manually measured values, and the corresponding measurement errors increased for larger trees;and 4) by considering the effect of the point cloud density correction, a transitional model can be fitted to approximate field measured DBH using LiBackpackscanned value with satisfactory accuracy(adjusted R2= 0.920;RMSE = 14.77 mm), and decrease the predicting error by 29.2%. Our study confirmed the reliability of the novel LiBackpack system in accurate forestry inventory, set up a useful transitional model between scanning data and the traditional manual-measured data specifically for P.sylvestris, and implied the applicable substitution of this new approach for more species, with necessary parameter calibration.