  Although various 3D datasets with different functions and scales have been
proposed recently, it remains challenging for individuals to complete the whole
pipeline of large-scale data collection, sanitization, and annotation.
Moreover, the created datasets usually suffer from extremely imbalanced class
distribution or partial low-quality data samples. Motivated by this, we explore
the procedurally synthetic 3D data generation paradigm to equip individuals
with the full capability of creating large-scale annotated photogrammetry point
clouds. Specifically, we introduce a synthetic aerial photogrammetry point
clouds generation pipeline that takes full advantage of open geospatial data
sources and off-the-shelf commercial packages. Unlike generating synthetic data
in virtual games, where the simulated data usually have limited gaming
environments created by artists, the proposed pipeline simulates the
reconstruction process of the real environment by following the same UAV flight
pattern on different synthetic terrain shapes and building densities, which
ensure similar quality, noise pattern, and diversity with real data. In
addition, the precise semantic and instance annotations can be generated fully
automatically, avoiding the expensive and time-consuming manual annotation.
Based on the proposed pipeline, we present a richly-annotated synthetic 3D
aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16
$km^2$ of landscapes and up to 18 fine-grained semantic categories. For
verification purposes, we also provide a parallel dataset collected from four
areas in the real environment. Extensive experiments conducted on our datasets
demonstrate the effectiveness and quality of the proposed synthetic dataset.
