RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian

• URL: http://arxiv.org/abs/2405.14342v3
• Date: Thu, 21 Nov 2024 12:32:08 GMT
• Title: RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian
• Authors: Zhiheng Feng, Wenhua Wu, Tianchen Deng, Hesheng Wang,
• Abstract summary: 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.
• Score: 10.50103969885774
• License:
• Abstract: Road surface reconstruction plays a crucial role in autonomous driving, which can be used for road lane perception and autolabeling. Recently, mesh-based road surface reconstruction algorithms have shown promising reconstruction results. However, these mesh-based methods suffer from slow speed and poor reconstruction quality. To address these limitations, we propose a novel large-scale road surface reconstruction approach with meshgrid Gaussian, named RoGs. Specifically, we model the road surface by placing Gaussian surfels in the vertices of a uniformly distributed square mesh, where each surfel stores color, semantic, and geometric information. This square mesh-based layout covers the entire road with fewer Gaussian surfels and reduces the overlap between Gaussian surfels during training. In addition, because the road surface has no thickness, 2D Gaussian surfel is more consistent with the physical reality of the road surface than 3D Gaussian sphere. Then, unlike previous initialization methods that rely on point clouds, we introduce a vehicle pose-based initialization method to initialize the height and rotation of the Gaussian surfel. Thanks to this meshgrid Gaussian modeling and pose-based initialization, our method achieves significant speedups while improving reconstruction quality. We obtain excellent results in reconstruction of road surfaces in a variety of challenging real-world scenes.

Related papers

• GSTAR: Gaussian Surface Tracking and Reconstruction [9.017056233547084]
  GSTAR is a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking for general dynamic scenes with changing topology. In regions where topology changes, GSTAR adaptively unbinds Gaussians from the mesh, enabling accurate registration and the generation of new surfaces. Our method effectively tracks and reconstructs dynamic surfaces, enabling a range of applications.
  arXiv  Detail & Related papers  (2025-01-17T16:26:24Z)
• SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction [48.228533595941556]
  We propose a novel method called SolidGS to address this problem. We observed that the reconstructed geometry can be severely inconsistent across multi-views. With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction.
  arXiv  Detail & Related papers  (2024-12-19T21:04:43Z)
• GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
  GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene. Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
  arXiv  Detail & Related papers  (2024-11-29T03:54:54Z)
• High-quality Surface Reconstruction using Gaussian Surfels [18.51978059665113]
  We propose a novel point-based representation, Gaussian surfels, to combine the advantages of the flexible optimization procedure in 3D Gaussian points. This is achieved by setting the z-scale of 3D Gaussian points to 0, effectively flattening the original 3D ellipsoid into a 2D ellipse. By treating the local z-axis as the normal direction, it greatly improves optimization stability and surface alignment.
  arXiv  Detail & Related papers  (2024-04-27T04:13:39Z)
• 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)
• NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance [59.08521048003009]
  We propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure. We introduce a scale regularizer to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely thin.
  arXiv  Detail & Related papers  (2023-12-01T07:04:47Z)
• 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)

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.