用轨迹图实现大场景任意视角渲染，解决重叠与分区难题
TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering
- 基于轨迹图动态分区，适配任意相机路径
- 比现有方法提升1.86dB（航拍）和1.62dB（地面）
- 适合大场景3D重建与高质量渲染任务
大尺度场景的高质量新视角合成在3D计算机视觉中仍具挑战。现有方法通常将大场景划分为多个区域，对每个区域使用高斯点阵重建3D表示，再合并用于新视角渲染。此类方法虽能精准还原特定场景，但难以泛化：(1) 固定空间划分难以适应任意相机轨迹；(2) 区域合并导致高斯点重叠，破坏纹理细节。为此，我们提出TraGraph-GS，利用轨迹图实现任意大场景的高精度渲染。设计一种基于图的分区方法，引入正则化约束以增强纹理与远距离物体渲染效果，并采用渐进式渲染策略缓解高斯重叠带来的伪影。实验表明，该方法在四个航拍和四个地面数据集上均表现优异，相比最先进方法，航拍数据平均提升1.86 dB PSNR，地面数据提升1.62 dB。
原文摘要 · Abstract (English)
High-quality novel view synthesis for large-scale scenes presents a challenging dilemma in 3D computer vision. Existing methods typically partition large scenes into multiple regions, reconstruct a 3D representation using Gaussian splatting for each region, and eventually merge them for novel view rendering. They can accurately render specific scenes, yet they do not generalize effectively for two reasons: (1) rigid spatial partition techniques struggle with arbitrary camera trajectories, and (2) the merging of regions results in Gaussian overlap to distort texture details. To address these challenges, we propose TraGraph-GS, leveraging a trajectory graph to enable high-precision rendering for arbitrarily large-scale scenes. We present a spatial partitioning method for large-scale scenes based on graphs, which incorporates a regularization constraint to enhance the rendering of textures and distant objects, as well as a progressive rendering strategy to mitigate artifacts caused by Gaussian overlap. Experimental results demonstrate its superior performance both on four aerial and four ground datasets and highlight its remarkable efficiency: our method achieves an average improvement of 1.86 dB in PSNR on aerial datasets and 1.62 dB on ground datasets compared to state-of-the-art approaches.
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