Controllable Road Marking Generation
Overview
This research introduces Controllable Road Marking Generation, a system that synthesizes road marking layouts from drivable-area masks and text descriptions. The method employs a text-conditioned latent rectified-flow DiT with topology-aware supervision and a Structured Gaussian Render post-process to recover crisp geometry. Evaluated on 4,597 Argoverse 2 test tiles, the system achieves a Buffered F1 of 76.8 and clDice of 50.2, outperforming an adapted mask-refinement baseline which scored 38.8 and 24.6 respectively. Independent training on the Waymo Open Dataset yielded scores of 88.0 for Buffered F1 and 66.2 for clDice.
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Reported quantity · test tiles: 4597 other · reported trials
“n for thin, sparse markings, and (iii) Structured Gaussian Render (SGR), a training-free post-process that recovers crisp divider geometry by extracting polylines, fitting cubic B\'ezier curves, and re-rendering them as anisotropic super-Gaussian primitives. On 4,597 Argoverse~2 test tiles, our system achieves Buffered F1 of 76.8 and clDice of 50.2, compared with 38.8 and 24.6 for an adapted mask-refinement baseline.”
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Reported quantity · Argoverse 2 test tiles: 4597 other · reported trials
“n for thin, sparse markings, and (iii) Structured Gaussian Render (SGR), a training-free post-process that recovers crisp divider geometry by extracting polylines, fitting cubic B\'ezier curves, and re-rendering them as anisotropic super-Gaussian primitives. On 4,597 Argoverse~2 test tiles, our system achieves Buffered F1 of 76.8 and clDice of 50.2, compared with 38.8 and 24.6 for an adapted mask-refinement baseline.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalControllable Road Marking Generation
This research introduces Controllable Road Marking Generation, a system that synthesizes road marking layouts from drivable-area masks and text descriptions. The method employs a text-conditioned latent rectified-flow DiT with topology-aware supervision and a Structured Gaussian Render post-process to recover crisp geometry. Evaluated on 4,597 Argoverse 2 test tiles, the system achieves a Buffered F1 of 76.8 and clDice of 50.2, outperforming an adapted mask-refinement baseline which scored 38.8 and 24.6 respectively. Independent training on the Waymo Open Dataset yielded scores of 88.0 for Buffered F1 and 66.2 for clDice.
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