NeuSOGA3D: A Neuro-Symbolic Framework for Explainable 3D Geometric Reconstruction
NeuSOGA3D combines learned perception with symbolic spline reasoning to produce explainable, CAD-compatible 3D reconstructions from point clouds across all forty ModelNet40 categories.
The hybrid neuro-symbolic framework projects point clouds onto principal orthographic planes, constructs symbolic implicit spline representations, and fuses them via shape-preserving constructive solid geometry into a coarse visual hull. Cross-sectional decomposition and Partial Shape-Preserving Splines recover additional detail, yielding explicit control polygons, implicit spline fields, cross-sections, and volumetric lofts. Experiments across all forty ModelNet40 categories demonstrate interpretable, engineering-workflow-compatible reconstruction from diverse point clouds.
- Hybrid neuro-symbolic framework fuses learned priors with symbolic spline geometry
- Produces explicit control polygons, spline fields, and volumetric lofts for interpretability
- Shape-preserving CSG operations build a coarse visual hull from orthographic projections
- Evaluated on all forty ModelNet40 categories for CAD-compatible reconstruction
- Products
- NeuSOGA3D
- Organizations
- ModelNet40
From the original article
1 min read at arxiv.orgThree-dimensional reconstruction from unorganized point clouds remains a challenging problem in computer vision, geometric modeling, and computer-aided design. While neural implicit methods achieve impressive reconstruction accuracy, geometry is typically encoded in latent representations that limit interpretability and reuse within engineering workflows.
By Qingde Li · arxiv.org
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