Dense Depth Estimation from Multiple 360-degree Images Using Virtual Depth
This work addresses depth estimation for 360-degree images, which is important for applications like virtual reality, but it appears incremental as it builds on existing spherical models and methods.
The paper tackles dense depth estimation from multiple 360-degree images by proposing a pipeline that extends a spherical camera model with translation scaling and uses virtual depth to minimize photonic reprojection error, resulting in improved accuracy over state-of-the-art methods.
In this paper, we propose a dense depth estimation pipeline for multiview 360° images. The proposed pipeline leverages a spherical camera model that compensates for radial distortion in 360° images. The key contribution of this paper is the extension of a spherical camera model to multiview by introducing a translation scaling scheme. Moreover, we propose an effective dense depth estimation method by setting virtual depth and minimizing photonic reprojection error. We validate the performance of the proposed pipeline using the images of natural scenes as well as the synthesized dataset for quantitive evaluation. The experimental results verify that the proposed pipeline improves estimation accuracy compared to the current state-of-art dense depth estimation methods.