VastGaussian：用於大型場景重建的大型3D高斯模型

摘要

現有基於 NeRF 的大場景重建方法通常在視覺品質和渲染速度上存在限制。儘管最近的 3D 高斯濺射方法在小規模和以物體為中心的場景上表現良好，但將其擴展到大場景會面臨限制的視頻內存、長時間優化和明顯的外觀變化等挑戰。為應對這些挑戰，我們提出了 VastGaussian，這是基於 3D 高斯濺射的大場景高質量重建和實時渲染的第一方法。我們提出了一種漸進式分割策略，將大場景劃分為多個單元，其中訓練相機和點雲根據空域感知可見性準則進行適當分佈。這些單元在並行優化後合併為完整場景。我們還將解耦的外觀建模引入優化過程中，以減少渲染圖像中的外觀變化。我們的方法優於現有的基於 NeRF 的方法，在多個大場景數據集上實現了最先進的結果，實現了快速優化和高保真實時渲染。

English

Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To address these challenges, we present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells, where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets, enabling fast optimization and high-fidelity real-time rendering.

VastGaussian：用於大型場景重建的大型3D高斯模型

VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction

摘要

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