Range-Agnostic Multi-View Depth Estimation With Keyframe Selection
Andrea Conti Matteo Poggi Valerio Cambareri Stefano Mattoccia
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.