SOURCE-LINKED INTELLIGENCE

CGGT: Curve-Grounded Geometry Transformer for 3D Parametric Curve Reconstruction

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Recovering editable 3D parametric curves from 2D images is a fundamental challenge in computer graphics, bridging pixel-based perception and vector-based CAD modeling. Existing NeRF- and 3DGS-based methods often rely on dense calibrated views, precomputed 2D edge maps, and costly per-scene optimization, limiting their applicability to casually captured real-world inputs. We propose CGGT, a Curve-Grounded Geometry Transformer that directly grounds 3D-consistent 2D curve instances in the image space from sparse, unposed multi-view images. CGGT combines a geometry-aware transformer encoder for mu

Read original source ↗ Open in workspace

recordType

paper

region

Global