arXiv:2508.06968cs.CVcs.GR2025-08ICCV被引 1

首次在200°鱼眼图像上验证3D高斯泼溅,发现160°视角效果最佳。

3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization

  • 用深度估计替代SfM进行初始化,解决超广角畸变下的重建失败问题。
  • 200°视角因畸变严重导致性能下降,160°时重建质量最优。
  • 首次将UniK3D用于超200°鱼眼图像,效果媲美甚至超越传统方法。

我们首次对真实鱼眼影像(视场角超过180°)上的3D高斯泼溅方法进行了评估。研究在200°鱼眼相机捕获的室内外场景中,对比了Fisheye-GS和3DGUT两种方法在200°、160°和120°视场角下的表现,发现两者在160°时达到最佳效果,兼顾了场景覆盖与图像质量,而200°时的严重畸变降低了性能。为解决超广角下结构光流(SfM)初始化常失败的问题,我们引入基于深度的替代方案——使用UniK3D(通用单目3D估计模型),尽管该模型未在如此宽视角数据上训练。通过控制预测点数与SfM一致以保证公平性,UniK3D生成的几何重建精度高,在雾、眩光或开阔天空等挑战性场景中表现优异,甚至优于传统SfM。结果证明鱼眼图像支持3D高斯泼溅的可行性,并为未来稀疏且高度畸变输入的宽视角重建研究提供了基准。

原文摘要 · Abstract (English)

We present the first evaluation of 3D Gaussian Splatting methods on real fisheye imagery with fields of view above 180\textdegree{}. Our study evaluates Fisheye-GS \cite{liao2024fisheyegslightweightextensiblegaussian} and 3DGUT \cite{wu20253dgut} on indoor and outdoor scenes captured with 200\textdegree{} fisheye cameras, with the aim of assessing the practicality of wide-angle reconstruction under severe distortion. By comparing reconstructions at 200\textdegree{}, 160\textdegree{}, and 120\textdegree{} field-of-view, we show that both methods achieve their best results at 160\textdegree{}, which balances scene coverage with image quality, while distortion at 200\textdegree{} degrades performance. To address the common failure of Structure-from-Motion (SfM) initialization at such wide angles, we introduce a depth-based alternative using UniK3D (Universal Camera Monocular 3D Estimation) \cite{piccinelli2025unik3d}. This represents the first application of UniK3D to fisheye imagery beyond 200\textdegree{}, despite the model not being trained on such data. With the number of predicted points controlled to match SfM for fairness, UniK3D produces geometrically accurate reconstructions that rival or surpass SfM, even in challenging scenes with fog, glare, or open sky. These results demonstrate the feasibility of fisheye-based 3D Gaussian Splatting and provides a benchmark for future research on wide-angle reconstruction from sparse and distorted inputs.

3D高斯鱼眼图像深度估计广角重建

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。