Methods for 3D reconstruction from posed frames require prior knowledge about the scene metric range, usually to recover matching cues along the epipolar lines and narrow the search range. However, such prior might not be directly available or estimated inaccurately in real scenarios -- e.g., outdoor 3D reconstruction from video sequences -- therefore heavily hampering performance. In this paper, we focus on multi-view depth estimation without requiring prior knowledge about the metric range of the scene by proposing RAMDepth, an efficient and purely 2D framework that reverses the depth estimation and matching steps order. Moreover, we demonstrate the capability of our framework to provide rich insights about the quality of the views used for prediction. Additional material can be found on our project page https://andreaconti.github.io/projects/range_agnostic_multi_view_depth.

翻译：基于已标定帧的三维重建方法需要场景度量范围的先验知识，通常用于沿极线恢复匹配线索并缩小搜索范围。然而，在实际场景（如从视频序列进行室外三维重建）中，这类先验可能无法直接获取或估计不准确，从而严重制约性能。本文聚焦于无需场景度量范围先验知识的多视角深度估计，提出了一种高效且纯二维的框架RAMDepth，该框架颠覆了深度估计与匹配步骤的执行顺序。此外，我们展示了该框架具备为预测所用视图质量提供丰富洞见的能力。补充材料详见项目页面 https://andreaconti.github.io/projects/range_agnostic_multi_view_depth。