S3R-GS: Streamlining the Pipeline for Large-Scale Street Scene Reconstruction

• URL: http://arxiv.org/abs/2503.08217v1
• Date: Tue, 11 Mar 2025 09:37:13 GMT
• Title: S3R-GS: Streamlining the Pipeline for Large-Scale Street Scene Reconstruction
• Authors: Guangting Zheng, Jiajun Deng, Xiaomeng Chu, Yu Yuan, Houqiang Li, Yanyong Zhang,
• Abstract summary: 3D Gaussian Splatting (3DGS) has reshaped the field of 3D reconstruction, achieving impressive rendering quality and speed.<n>Existing methods suffer from rapidly escalating per-viewpoint reconstruction costs as scene size increases.<n>We propose S3R-GS, a 3DGS framework that Streamlines the pipeline for large-scale Street Scene Reconstruction.
• Score: 58.37746062258149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, 3D Gaussian Splatting (3DGS) has reshaped the field of photorealistic 3D reconstruction, achieving impressive rendering quality and speed. However, when applied to large-scale street scenes, existing methods suffer from rapidly escalating per-viewpoint reconstruction costs as scene size increases, leading to significant computational overhead. After revisiting the conventional pipeline, we identify three key factors accounting for this issue: unnecessary local-to-global transformations, excessive 3D-to-2D projections, and inefficient rendering of distant content. To address these challenges, we propose S3R-GS, a 3DGS framework that Streamlines the pipeline for large-scale Street Scene Reconstruction, effectively mitigating these limitations. Moreover, most existing street 3DGS methods rely on ground-truth 3D bounding boxes to separate dynamic and static components, but 3D bounding boxes are difficult to obtain, limiting real-world applicability. To address this, we propose an alternative solution with 2D boxes, which are easier to annotate or can be predicted by off-the-shelf vision foundation models. Such designs together make S3R-GS readily adapt to large, in-the-wild scenarios. Extensive experiments demonstrate that S3R-GS enhances rendering quality and significantly accelerates reconstruction. Remarkably, when applied to videos from the challenging Argoverse2 dataset, it achieves state-of-the-art PSNR and SSIM, reducing reconstruction time to below 50%--and even 20%--of competing methods.

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