AI RESEARCH
ReManNet: A Riemannian Manifold Network for Monocular 3D Lane Detection
arXiv CS.CV
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ArXi:2603.19776v1 Announce Type: new Monocular 3D lane detection remains challenging due to depth ambiguity and weak geometric constraints. Mainstream methods rely on depth guidance, BEV projection, and anchor- or curve-based heads with simplified physical assumptions, remapping high-dimensional image features while only weakly encoding road geometry. Lacking an invariant geometric-topological coupling between lanes and the underlying road surface, 2D-to-3D lifting is ill-posed and brittle, often degenerating into concavities, bulges, and twists.