用单个3D基础模型3分钟完成稀疏无标定图像重建，精度领先。
Sparfels: Fast Reconstruction from Sparse Unposed Imagery
- 基于3D基础模型的点图和相机初值，快速构建2D高斯点云并优化
- 通过减少射线颜色方差提升形状重建精度，在多视图数据集上达最优
- 适合需要快速、高精度三维重建的科研与工业场景
我们提出一种基于表面元素点云的稀疏视图重建方法，在消费级GPU上可在3分钟内完成。尽管已有少量方法针对噪声大或未标定的稀疏相机进行辐射场学习，但形状恢复在此类设置中仍较少被研究。现有方法多依赖数据先验或外部单目几何先验来处理稀疏标定情况。而本文提出一种高效简洁的流水线，仅使用一个近期的3D基础模型。利用其多个任务头，特别是点图与相机初始化，构建并优化2D高斯点云（2DGS）模型，并通过图像对应关系指导2DGS训练中的相机优化。关键贡献在于提出一种可高效计算的射线色差新公式，训练中降低该矩能显著提升形状重建精度。在基于标准多视图数据集的重建与新视角生成基准测试中，本方法在稀疏非标定设置下达到当前最优表现。
原文摘要 · Abstract (English)
We present a method for Sparse view reconstruction with surface element splatting that runs within 3 minutes on a consumer grade GPU. While few methods address sparse radiance field learning from noisy or unposed sparse cameras, shape recovery remains relatively underexplored in this setting. Several radiance and shape learning test-time optimization methods address the sparse posed setting by learning data priors or using combinations of external monocular geometry priors. Differently, we propose an efficient and simple pipeline harnessing a single recent 3D foundation model. We leverage its various task heads, notably point maps and camera initializations to instantiate a bundle adjusting 2D Gaussian Splatting (2DGS) model, and image correspondences to guide camera optimization midst 2DGS training. Key to our contribution is a novel formulation of splatted color variance along rays, which can be computed efficiently. Reducing this moment in training leads to more accurate shape reconstructions. We demonstrate state-of-the-art performances in the sparse uncalibrated setting in reconstruction and novel view benchmarks based on established multi-view datasets.
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