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The recent surge in diverse 3D datasets spanning various scales and applications marks a significant advancement in the field. However, the comprehensive process of data acquisition, refinement, and annotation at a large scale poses a formidable challenge, particularly for individual researchers and small teams. To this end, we present a novel synthetic 3D point cloud generation framework that can produce detailed outdoor aerial photogrammetric 3D datasets with accurate ground truth annotations without the labor-intensive and time-consuming data collection/annotation processes. Our pipeline procedurally generates synthetic environments, mirroring real-world data collection and 3D reconstruction processes. A key feature of our framework is its ability to replicate consistent quality, noise patterns, and diversity similar to real-world datasets. This is achieved by adopting UAV flight patterns that resemble those used in real-world data collection processes (e.g., the cross-hatch flight pattern) across various synthetic terrains that are procedurally generated, thereby ensuring data consistency akin to real-world scenarios. Moreover, the generated datasets are enriched with precise semantic and instance annotations, eliminating the need for manual labeling. Our approach has led to the development and release of the Semantic Terrain Points Labeling—Synthetic 3D (STPLS3D) benchmark, an extensive outdoor 3D dataset encompassing over 16 km 2 , featuring up to 19 semantic labels. We also collected, reconstructed, and annotated four real-world datasets for validation purposes. Extensive experiments on these datasets demonstrate our synthetic datasets' effectiveness, superior quality, and their value as a benchmark dataset for further point cloud research. [ABSTRACT FROM AUTHOR]

A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%. [ABSTRACT FROM AUTHOR]

The US Army is paying increased attention to the development of rapid three-dimensional (3D) reconstruction using photogrammetry and unmanned aerial vehicle (UAV) technologies for creating virtual environments and simulations in areas of interest. The ability of the intelligence community, mission commanders, and front-line soldiers to understand their deployed physical environment in advance is critical in the planning and rehearsal phases of any military operation. In order to achieve various simulation capabilities such as destruction operations, route planning, and explosive-standoff distances computation among others, reconstructed 3D data needs to be properly attributed. In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i.e., ground, human-made objects, and vegetation). Preprocessing and postprocessing methods were designed to overcome the data segmentation challenges posed by photogrammetric data-quality issues. A large UAV-based photogrammetric database was created for validation purposes. The designed model ensembling framework was compared with existing point cloud segmentation algorithms, and it outperformed other algorithms and achieved the best F1-score. Because the ultimate goal of segmenting a photogrammetric-generated point cloud is to create realistic virtual environments for simulation. Qualitative results for creating virtual environments using the segmented data are also discussed in this paper. [ABSTRACT FROM AUTHOR]