Sparfels：从稀疏无位姿图像中快速重建
Sparfels: Fast Reconstruction from Sparse Unposed Imagery
AI总结：
针对稀疏无位姿图像重建难题，提出Sparfels方法，借助单个3D基础模型与新型溅射颜色方差公式优化2DGS训练，在消费级GPU上3分钟内实现稀疏未标定场景的最优重建效果。
摘要：
我们提出了一种基于面元溅射的稀疏视角重建方法，在消费级GPU上运行时间不超过3分钟。目前针对噪声或无位姿稀疏相机的稀疏辐射场学习方法较少，该场景下的形状恢复研究仍相对不足。现有多种辐射场与形状学习的测试时优化方法通过学习数据先验或结合外部单目几何先验，解决有位姿的稀疏重建问题。与之不同，我们提出了一种高效简洁的流水线，仅利用一个最新的3D基础模型。我们借助其多种任务头（尤其是点图与相机初始化模块）来实例化可进行光束法平差的2D Gaussian Splatting（2DGS，二维高斯溅射）模型，并利用图像对应关系在2DGS训练过程中引导相机优化。我们的核心贡献是提出了一种沿光线的溅射颜色方差的新公式，可高效计算。在训练中降低该矩量可提升形状重建的准确性。我们在基于成熟多视图数据集的稀疏未标定场景重建与新视角基准测试中，取得了当前最优性能。
英文摘要：
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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