用统一体积高效生成城市场景3D高斯点，实现实时渲染。
EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis
- 通过3D卷积网络统一预测多帧3D高斯点，避免重复和视图不一致
- 基于噪声深度初始化，3D空间优化几何并结合2D纹理预测颜色
- 支持远距离视角与天空建模，适合自动驾驶场景快速重建
城市场景的新视角合成对自动驾驶应用至关重要。现有NeRF和3DGS方法虽能生成逼真图像，但需逐场景优化，速度慢。本文提出EVolSplat，一种面向城市场景的高效3D高斯点渲染模型，支持前向传播。不同于现有前向传播、像素对齐的3DGS方法常出现多视角不一致和内容重复的问题，我们的方法利用3D卷积网络在统一体积内预测多帧3D高斯点。通过噪声深度初始化，再在3D空间中优化其几何属性，并基于2D纹理预测颜色。模型还采用灵活的半球背景模型处理远距离视角与天空。该设计实现快速前向重建与实时渲染。在KITTI-360和Waymo数据集上的实验表明，本方法在前向传播的3DGS与NeRF类方法中达到最先进水平。
原文摘要 · Abstract (English)
Novel view synthesis of urban scenes is essential for autonomous driving-related applications.Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization. We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner. Unlike existing feed-forward, pixel-aligned 3DGS methods, which often suffer from issues like multi-view inconsistencies and duplicated content, our approach predicts 3D Gaussians across multiple frames within a unified volume using a 3D convolutional network. This is achieved by initializing 3D Gaussians with noisy depth predictions, and then refining their geometric properties in 3D space and predicting color based on 2D textures. Our model also handles distant views and the sky with a flexible hemisphere background model. This enables us to perform fast, feed-forward reconstruction while achieving real-time rendering. Experimental evaluations on the KITTI-360 and Waymo datasets show that our method achieves state-of-the-art quality compared to existing feed-forward 3DGS- and NeRF-based methods.
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