MonSter: Awakening the Mono in Stereo
This work addresses depth estimation challenges in computer vision by integrating two common methods, offering incremental improvements for applications requiring robust depth sensing.
The authors tackled the problem of passive depth estimation by combining stereo and monocular camera systems to improve accuracy and enable online self-calibration, demonstrating benefits in real-world scenes with a prototype camera.
Passive depth estimation is among the most long-studied fields in computer vision. The most common methods for passive depth estimation are either a stereo or a monocular system. Using the former requires an accurate calibration process, and has a limited effective range. The latter, which does not require extrinsic calibration but generally achieves inferior depth accuracy, can be tuned to achieve better results in part of the depth range. In this work, we suggest combining the two frameworks. We propose a two-camera system, in which the cameras are used jointly to extract a stereo depth and individually to provide a monocular depth from each camera. The combination of these depth maps leads to more accurate depth estimation. Moreover, enforcing consistency between the extracted maps leads to a novel online self-calibration strategy. We present a prototype camera that demonstrates the benefits of the proposed combination, for both self-calibration and depth reconstruction in real-world scenes.