解决广角镜头下大视场重建难题，实现高精度、少图像重建。
Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction
- 联合优化相机参数、畸变和3D高斯表示，自校准重建过程。
- 采用混合网络建模复杂畸变，精度优于传统相机模型。
- 适配多种镜头畸变，适合真实场景与合成数据的快速重建。
本文提出一种自校准框架，联合优化相机参数、镜头畸变和3D高斯表示，实现高精度、高效的场景重建。特别地，该方法能从使用广角镜头拍摄的大视场（FOV）图像中重建高质量场景，仅需较少图像即可完成建模。我们提出一种新型畸变建模方法，结合可逆残差网络与显式网格，有效正则化优化过程，显著提升精度。此外，设计基于立方体贴图的重采样策略，在不损失分辨率或引入畸变伪影的前提下支持大视场图像。本方法兼容高斯点云的快速光栅化，适用于多种镜头畸变，且在合成与真实数据集上均达到当前最佳性能。
原文摘要 · Abstract (English)
In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. In particular, our technique enables high-quality scene reconstruction from Large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. Our approach introduces a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortion, and demonstrates state-of-the-art performance on both synthetic and real-world datasets.
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