Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction

• URL: http://arxiv.org/abs/2507.23006v1
• Date: Wed, 30 Jul 2025 18:11:55 GMT
• Title: Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction
• Authors: Zhensheng Yuan, Haozhi Huang, Zhen Xiong, Di Wang, Guanghua Yang,
• Abstract summary: We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes.<n>Our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality.
• Score: 10.948843569839305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for parallel training, employing a visibility-based image selection strategy to optimize training efficiency. A controllable level-of-detail (LOD) strategy explicitly regulates Gaussian density under a user-defined budget, enabling efficient training and rendering while maintaining high visual fidelity. The appearance transformation module mitigates the negative effects of appearance inconsistencies across images while enabling flexible adjustments. Additionally, we utilize enhancement modules, such as depth regularization, scale regularization, and antialiasing, to improve reconstruction fidelity. Experimental results demonstrate that our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality. The source code is available at: https://yzslab.github.io/REUrbanGS.

Related papers

• Appearance Harmonization via Bilateral Grid Prediction with Transformers for 3DGS [17.21080750486132]
  Camera pipelines apply extensive on-device processing, such as exposure adjustment, white balance, and color correction.<n>These appearance variations violate multi-view consistency and degrade the quality of novel view synthesis.<n>Joint optimization of scene representations and per-image appearance embeddings has been proposed to address this issue, but at the cost of increased computational complexity and slower training.<n>We propose a transformer-based method that predicts spatially adaptive bilateral grids to correct photometric variations in a multi-view consistent manner.
  arXiv  Detail & Related papers  (2025-07-21T16:03:58Z)
• HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis [59.25751939710903]
  We propose a novel deformable Gaussian splatting framework that addresses embodied view synthesis from long monocular RGB videos.<n>Our method leverages invertible Gaussian Splatting deformation networks to reconstruct large-scale, dynamic environments accurately.<n>Results highlight a practical and scalable solution for EVS in real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-24T03:54:40Z)
• RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS [79.15416002879239]
  3D Gaussian Splatting has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling.<n>Existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images.<n>We propose RobustSplat, a robust solution based on two critical designs.
  arXiv  Detail & Related papers  (2025-06-03T11:13:48Z)
• Gaussian On-the-Fly Splatting: A Progressive Framework for Robust Near Real-Time 3DGS Optimization [8.422116335889163]
  3D Gaussian Splatting (3DGS) achieves high-fidelity rendering with fast real-time performance.<n>Existing methods rely on offline training after full Structure-from-Motion (SfM) processing.<n>We introduce On-the-Fly GS, a progressive framework enabling near real-time 3DGS optimization during image capture.
  arXiv  Detail & Related papers  (2025-03-17T11:47:58Z)
• Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement [50.93686436282772]
  We aim to delve into the limits of image enhancers both from visual quality and computational efficiency.<n>By rethinking the task demands, we build an explicit connection, i.e., visual quality and computational efficiency are corresponding to model learning and structure design.<n>Ultimately, this achieves efficient low-light image enhancement using only a single convolutional layer, while maintaining excellent visual quality.
  arXiv  Detail & Related papers  (2025-02-27T08:20:03Z)
• Efficient Transformer for High Resolution Image Motion Deblurring [0.0]
  This paper presents a comprehensive study and improvement of the Restormer architecture for high-resolution image motion deblurring.<n>We introduce architectural modifications that reduce model complexity by 18.4% while maintaining or improving performance through optimized attention mechanisms.<n>Our results suggest that thoughtful architectural simplification combined with enhanced training strategies can yield more efficient yet equally capable models for motion deblurring tasks.
  arXiv  Detail & Related papers  (2025-01-30T14:58:33Z)
• SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians [2.2369578015657954]
  Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections. We introduce a new mechanism to train transient Gaussians to handle the presence of scene occluders in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-03-15T16:00:04Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)
• Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering [71.44349029439944]
  Recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering.
  arXiv  Detail & Related papers  (2023-11-30T17:58:57Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)
• DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation [56.514462874501675]
  We propose a dynamic sparse attention based Transformer model to achieve fine-level matching with favorable efficiency. The heart of our approach is a novel dynamic-attention unit, dedicated to covering the variation on the optimal number of tokens one position should focus on. Experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistorted image style transfer, demonstrate that DynaST achieves superior performance in local details.
  arXiv  Detail & Related papers  (2022-07-13T11:12:03Z)

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.