Superquadric Primitive Decomposition of 3D point clouds via Geometric-Aware Inlier Refinement
This study addresses the susceptibility of superquadric decomposition of 3D point clouds to noise, outliers, and overlapping structures, which frequently leads to erroneous inlier misassignment. To overcome this limitation, we propose a geometry-aware framework that transcends conventional residual-based criteria by explicitly incorporating local geometric priors, such as normal consistency, into the fitting process. Furthermore, graph-cut optimization is employed to minimize an energy function for precise inlier refinement. This approach effectively suppresses the propagation of false inliers and stabilizes parameter estimation. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed method significantly outperforms RANSAC in terms of geometric accuracy, robustness to noise, and convergence efficiency.