Year: 2025
Type: software-paper
Abstract: Efficient and robust space partitioning of 3D space underpins many applications in computer graphics.abspy is a Python package for adaptive binary space partitioning and 3D modeling.At its core, abspy constructs a plane arrangement by recursively subdividing 3D space with planar primitives to form a linear cell complex that reflects the underlying geometric structure.This adaptive scheme iteratively refines the spatial decomposition, producing a compact representation that supports efficient 3D modeling and analysis.Built on robust arithmetic kernels and interoperable data structures, abspy exposes a high-level Python API that lets researchers prototype advanced 3D pipelines such as surface reconstruction, volumetric analysis, and feature extraction for machine learning. Statement of needIn many scientific and engineering workflows, raw 3D data must be transformed into compact volumetric representations for visualization, simulation, and analysis of complex 3D environments.Sensor-acquired point clouds and photogrammetric meshes often contain hundreds of millions of irregular, noisy samples that are expensive to store, transmit, and query, and that provide little inherent structure.Converting such data into a well-defined spatial decomposition, in which each cell represents a coherent geometric region, reduces memory usage, spares downstream algorithms from ad-hoc filtering and enables exact spatial predicates such as intersection, containment, and adjacency.The central challenge is to obtain this decomposition without inheriting the aliasing artefacts of coarse voxel grids or the combinatorial explosion produced by exhaustive plane arrangements (Edelsbrunner et al., 1986).
DOI: 10.21105/joss.07946
Author: Zhaiyu Chen
Institution: Technical University of Munich
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411086789