A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

Abstract

Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm2. The MAE values for small leaves, medium leaves and large leaves were 0.92 cm2, 1.05 cm2 and 3.39 cm2, respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.

Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

“…LiDAR is an active remote sensing technology that emits laser pulses to the surface of vegetative elements and analyses the return signal [5]. The point cloud data obtained from LiDAR are valuable and convenient for estimating a variety of tree attributes, e.g., leaf area [6], phenotypic characteristics of leaf elements [7], tree structural attributes [8] and volume of timber [9]. In recent years, the efficiency of measurements has been greatly improved by mobile LiDAR, such as LiDAR loaded on ground vehicles [10], humans (hand-held [11] and backpack [12] modes) and manned aircraft.…”

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

“…In previous works, e.g., [19,28,[67][68][69], it was found that noise reduction during preprocessing yielded a better digital terrain model, but on the other hand this procedure could sometimes reduce the accuracy of the model, as [70] found in their study. We had a hypothesis that the noise reduction of the point cloud results in more accurate models in our case, and we pointed out that different noise reduction techniques can have a significant effect on the input data which are the basis of the next stage of the process, i.e., ground point classification.…”

“…Noise is considered to consist of outliers which have different characteristics than the neighboring points, i.e., supposing a locally planar area (i.e., kernel window) defined by the average distance from a center or considering its k neighbors, when outlying points fall outside it. Noise filtering can be based on principal component analysis [19], neighborhood distance [20,21], or distance from surface [22].…”

Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data

“…Biological parameters extracted from TLS data, such as the gap fraction [27], tree volume [28], leaf area density [29] and tree diameter at breast height [30], can be successfully assessed without the need for arduous manual measurements. Furthermore, many algorithms for trees using TLS data have been derived, such as algorithms for individual leaf property extraction [31], leaf phenotypic characteristic calculation [32] and photosynthetic and non-photosynthetic part separation [33]. TLS is becoming increasingly useful for providing information to support forest observations, although the terms of the algorithm aspect for the detailed description of the whole tree from scanned data still require further improvement.…”