无需固定视角，通过排列等变设计实现更稳定的视觉几何重建。
$π^3$: Permutation-Equivariant Visual Geometry Learning
- 采用排列等变架构，不依赖任何参考视角
- 在相机位姿、深度估计等任务上达顶尖性能
- 适合需要消除视角偏见的三维视觉应用
我们提出 $π^3$，一种前馈神经网络，为视觉几何重建提供新范式，摆脱对传统固定参考视角的依赖。以往方法常以特定视角为锚点，若参考视角不佳则易导致不稳定甚至失败。$π^3$ 采用完全排列等变架构，无需参考帧即可预测仿射不变的相机位姿和尺度不变的局部点图。该设计不仅使模型天然对输入顺序鲁棒，还显著提升精度与性能。这一简洁无偏的方法在相机位姿估计、单目/视频深度估计及稠密点图重建等多项任务中均达到当前最优表现。代码与模型已开源：https://github.com/yyfz/Pi3。
原文摘要 · Abstract (English)
We introduce $π^3$, a feed-forward neural network that offers a novel approach to visual geometry reconstruction, breaking the reliance on a conventional fixed reference view. Previous methods often anchor their reconstructions to a designated viewpoint, an inductive bias that can lead to instability and failures if the reference is suboptimal. In contrast, $π^3$ employs a fully permutation-equivariant architecture to predict affine-invariant camera poses and scale-invariant local point maps without any reference frames. This design not only makes our model inherently robust to input ordering, but also leads to higher accuracy and performance. These advantages enable our simple and bias-free approach to achieve state-of-the-art performance on a wide range of tasks, including camera pose estimation, monocular/video depth estimation, and dense point map reconstruction. Code and models are available at https://github.com/yyfz/Pi3.
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。