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QuadricsReg: Large-Scale Point Cloud Registration Using Semantic Quadric Primitives
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AI
DOI：10.1109/TRO.2026.3686263.png)
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Abstract
En 中文
Designing an effective and scalable scene primitive representation is fundamental for large-scale point cloud registration. Existing studies that predominantly rely on dense point clouds or single-type geometric primitives struggle to scale to scenes characterized by massive data volume, structural diversity, and wide viewpoint variations. To address these, this article introduces QuadricsReg, a novel point cloud registration framework based on quadric primitives for large-scale environments. We compactly model diverse scene structures within a unified semantic quadric formulation, achieving high compression while preserving geometric richness and discriminability. This representation enables efficient quadric matching initialization via intrinsic similarity and robust correspondence pruning by maximizing geometric consistency in a multilevel graph, ensuring reliable associations even under large viewpoint variations. Furthermore, we design a factor graph based on degeneracy-aware quadric residual to estimate the transformation, ensuring accurate alignment in heterogeneous scenes. We evaluate QuadricsReg on 5 public datasets, where its exceptional registration performance with low overhead demonstrates strong scalability for large-scale scenarios. With a compact representation of $\sim 29.5 \mathrm{KB/scan}$ on KITTI, nearly 100% registration success rate is achieved for point cloud pairs within $10 \mathrm{m}$. Real-world testing on the self-collected dataset further validates its robustness and generalization ability across different LiDAR sensors and robot platforms.
Keywords:
Geometric primitives
localization and mapping
point cloud registration
quadric representation