SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes
Nannan Qin, Pengjie Tao, Haiyan Guan, Zhizhong Kang, Lingfei Ma, Xiangyun Hu, Jonathan Li

TL;DR
SparseGF is a novel height-aware sparse segmentation framework with context compression that significantly improves ground filtering accuracy across diverse terrains in airborne laser scanning data.
Contribution
It introduces a convex-mirror-inspired context compression, a hybrid sparse voxel-point network, and a height-aware loss for better cross-scene generalization in ground filtering.
Findings
Achieves leading performance in urban scenes
Demonstrates robustness across natural terrains
Performs well in complex and steep forested areas
Abstract
High-quality digital terrain models derived from airborne laser scanning (ALS) data are essential for a wide range of geospatial analyses, and their generation typically relies on robust ground filtering (GF) to separate point clouds across diverse landscapes into ground and non-ground parts. Although current deep-learning-based GF methods have demonstrated impressive performance, especially in specific challenging terrains, their cross-scene generalization remains limited by two persistent issues: the context-detail dilemma in large-scale processing due to limited computational resources, and the random misclassification of tall objects arising from classification-only optimization. To overcome these limitations, we propose SparseGF, a height-aware sparse segmentation framework enhanced with context compression. It is built upon three key innovations: (1) a convex-mirror-inspired…
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