GaussianPro：具有漸進傳播的3D高斯點陣化

摘要

最近，3D 高斯飛濺（3DGS）的出現在神經渲染領域引發了一場革命，實現了高質量渲染的實時速度。然而，3DGS 在很大程度上依賴由運動結構（SfM）技術產生的初始化點雲。當處理不可避免包含無紋理表面的大型場景時，SfM 技術總是無法在這些表面產生足夠的點，並且無法為 3DGS 提供良好的初始化。因此，3DGS 面臨著困難的優化和低質量渲染。在本文中，受到經典多視圖立體（MVS）技術的啟發，我們提出了 GaussianPro，一種新穎的方法，應用漸進式傳播策略來引導 3D 高斯飛濺的密集化。與 3DGS 中使用的簡單分割和克隆策略相比，我們的方法利用場景現有重建幾何的先驗知識和補丁匹配技術來生成具有準確位置和方向的新高斯飛濺。在大型和小型場景上的實驗驗證了我們方法的有效性，在 Waymo 數據集上，我們的方法明顯優於 3DGS，PSNR 方面提高了 1.15dB。

English

The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initialized point cloud produced by Structure-from-Motion (SfM) techniques. When tackling with large-scale scenes that unavoidably contain texture-less surfaces, the SfM techniques always fail to produce enough points in these surfaces and cannot provide good initialization for 3DGS. As a result, 3DGS suffers from difficult optimization and low-quality renderings. In this paper, inspired by classical multi-view stereo (MVS) techniques, we propose GaussianPro, a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Compared to the simple split and clone strategies used in 3DGS, our method leverages the priors of the existing reconstructed geometries of the scene and patch matching techniques to produce new Gaussians with accurate positions and orientations. Experiments on both large-scale and small-scale scenes validate the effectiveness of our method, where our method significantly surpasses 3DGS on the Waymo dataset, exhibiting an improvement of 1.15dB in terms of PSNR.

GaussianPro：具有漸進傳播的3D高斯點陣化

GaussianPro: 3D Gaussian Splatting with Progressive Propagation

摘要

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