RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping

• URL: http://arxiv.org/abs/2607.15048v1
• Date: Thu, 16 Jul 2026 14:26:52 GMT
• Title: RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
• Abstract summary: Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation and lane-level perception.<n>Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction quality and high optimization cost.<n>We propose a robust and efficient large-scale road surface mapping framework based on adaptive meshgrid Gaussian representation.
• Score: 20.534227619549487
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation, lane-level perception, and automatic road annotation. Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction quality and high optimization cost, especially in large-scale driving scenarios. To address these limitations, we propose ROADGS-T, a robust and efficient large-scale road surface mapping framework based on adaptive meshgrid Gaussian representation. Specifically, we model the road surface by placing 2D Gaussian surfels on a meshgrid, where each surfel explicitly stores color, semantic, and geometric information. Compared with conventional mesh-based representations and 3D Gaussian primitives, the proposed meshgrid Gaussian representation better matches the thin-surface property of roads while significantly reducing redundant primitives and overlap during optimization. To further improve representation efficiency and structural fidelity, we introduce a road-structure-aware adaptive meshgrid strategy, which allocates denser Gaussian surfels to geometrically or semantically complex regions, such as lane markings, road boundaries, and height discontinuities, while maintaining a compact representation in flat road areas. Moreover, instead of relying on a single nearest vehicle pose, we design a trajectory-consistency-guided pose-robust refinement strategy, which estimates local surface priors from multiple neighboring poses and adaptively weights pose-guided height regularization according to their geometric consistency.

Related papers

• RoadVGGT: Road-Structure-Aware Feed-Forward Road Surface Reconstruction [18.76852385853567]
  RoadVGGT is a feed-forward framework that reconstructs compact Gaussian road surfaces without test-time per-scene optimization.<n>It supports RGB and semantic bird's-eye-view maps, elevation estimation, and novel view synthesis.
  arXiv  Detail & Related papers  (2026-07-26T17:09:12Z)
• Shape Your Ground: Refining Road Surfaces Beyond Planar Representations [35.63881467885378]
  Road surface reconstruction from aerial images is fundamental for autonomous driving, urban planning, and virtual simulation.<n>Existing reconstruction methods often produce artifacts and inconsistencies that limit usability.<n>We introduce FlexRoad, the first framework to address road surface smoothing by fitting Non-Uniform Rational B-Splines (NURBS) surfaces to 3D road points obtained from photogrammetric reconstructions or geodata providers.
  arXiv  Detail & Related papers  (2025-04-15T21:20:44Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation.<n>It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
  StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
  arXiv  Detail & Related papers  (2024-10-06T04:21:59Z)
• RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian [10.50103969885774]
  Road surface reconstruction plays a crucial role in autonomous driving. We propose a novel large-scale road surface reconstruction approach with meshgrid Gaussian, named RoGs. We obtain excellent results in reconstruction of road surfaces in a variety of challenging real-world scenes.
  arXiv  Detail & Related papers  (2024-05-23T09:11:47Z)
• EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding [21.117919848535422]
  EMIE-MAP is a novel method for large-scale road surface reconstruction based on explicit mesh and implicit encoding. Our method achieves remarkable road surface reconstruction performance in a variety of real-world challenging scenarios.
  arXiv  Detail & Related papers  (2024-03-18T13:46:52Z)
• GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering [83.19049705653072]
  During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved. We propose a novel approach called GeoGaussian to mitigate this issue. Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction.
  arXiv  Detail & Related papers  (2024-03-17T20:06:41Z)
• Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
  It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion. We develop a novel GS-based method that enables interactive deformation. Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
  arXiv  Detail & Related papers  (2024-02-07T12:36:54Z)
• GridFormer: Point-Grid Transformer for Surface Reconstruction [37.30776475324579]
  We introduce a novel attention mechanism between the grid and point features named Point-Grid Transformer (GridFormer) This mechanism treats the grid as a transfer point connecting the space and point cloud. We also propose a boundary optimization strategy incorporating margin binary cross-entropy loss and boundary sampling.
  arXiv  Detail & Related papers  (2024-01-04T14:31:56Z)
• Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement [50.56517624931987]
  The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. This work aims to reconstruct high-fidelity surfaces with fine-grained details by addressing the above limitations.
  arXiv  Detail & Related papers  (2023-09-14T12:05:29Z)
• Convolutional Recurrent Network for Road Boundary Extraction [99.55522995570063]
  We tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. We design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time.
  arXiv  Detail & Related papers  (2020-12-21T18:59:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.