MagicRoad: Semantic-Aware 3D Road Surface Reconstruction via Obstacle Inpainting

• URL: http://arxiv.org/abs/2507.23340v1
• Date: Thu, 31 Jul 2025 08:38:36 GMT
• Title: MagicRoad: Semantic-Aware 3D Road Surface Reconstruction via Obstacle Inpainting
• Authors: Xingyue Peng, Yuandong Lyu, Lang Zhang, Jian Zhu, Songtao Wang, Jiaxin Deng, Songxin Lu, Weiliang Ma, Dangen She, Peng Jia, XianPeng Lang,
• Abstract summary: Road surface reconstruction is essential for autonomous driving, supporting centimeter-accurate lane perception and high-definition mapping in complex urban environments.<n>We present a robust reconstruction framework that integrates 2D Gaussian surfels with semantic-guided color enhancement to recover clean, consistent road surfaces.
• Score: 4.090597563540577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Road surface reconstruction is essential for autonomous driving, supporting centimeter-accurate lane perception and high-definition mapping in complex urban environments.While recent methods based on mesh rendering or 3D Gaussian splatting (3DGS) achieve promising results under clean and static conditions, they remain vulnerable to occlusions from dynamic agents, visual clutter from static obstacles, and appearance degradation caused by lighting and weather changes. We present a robust reconstruction framework that integrates occlusion-aware 2D Gaussian surfels with semantic-guided color enhancement to recover clean, consistent road surfaces. Our method leverages a planar-adapted Gaussian representation for efficient large-scale modeling, employs segmentation-guided video inpainting to remove both dynamic and static foreground objects, and enhances color coherence via semantic-aware correction in HSV space. Extensive experiments on urban-scale datasets demonstrate that our framework produces visually coherent and geometrically faithful reconstructions, significantly outperforming prior methods under real-world conditions.

Related papers

• 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)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• 3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene Reconstruction [1.2603104712715607]
  This paper proposes a novel 3D Gaussian point distribution method for dynamic street scene reconstruction.<n>Our approach eliminates moving objects while preserving high-fidelity static scene details.<n> Experimental results demonstrate that our method achieves high reconstruction quality, improved rendering performance, and adaptability in large-scale dynamic environments.
  arXiv  Detail & Related papers  (2025-03-15T05:41:59Z)
• MVGSR: Multi-View Consistency Gaussian Splatting for Robust Surface Reconstruction [46.081262181141504]
  3D Gaussian Splatting (3DGS) has gained significant attention for its high-quality rendering capabilities, ultra-fast training, and inference speeds.<n>We propose Multi-View Consistency Gaussian Splatting for the domain of Robust Surface Reconstruction (textbfMVGSR)<n>MVGSR achieves competitive geometric accuracy and rendering fidelity compared to the state-of-the-art surface reconstruction algorithms.
  arXiv  Detail & Related papers  (2025-03-11T06:53:27Z)
• UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction [86.4386398262018]
  UrbanGS uses 2D semantic maps and an existing dynamic Gaussian approach to distinguish static objects from the scene.<n>For potentially dynamic objects, we aggregate temporal information using learnable time embeddings.<n>Our approach outperforms state-of-the-art methods in reconstruction quality and efficiency.
  arXiv  Detail & Related papers  (2024-12-04T16:59:49Z)
• T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
  Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-11-29T07:45:24Z)
• 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)
• GUS-IR: Gaussian Splatting with Unified Shading for Inverse Rendering [83.69136534797686]
  We present GUS-IR, a novel framework designed to address the inverse rendering problem for complicated scenes featuring rough and glossy surfaces. This paper starts by analyzing and comparing two prominent shading techniques popularly used for inverse rendering, forward shading and deferred shading. We propose a unified shading solution that combines the advantages of both techniques for better decomposition.
  arXiv  Detail & Related papers  (2024-11-12T01:51:05Z)
• SMORE: Simultaneous Map and Object REconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR.<n>We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)

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.