Depth estimation with occlusion handling from a sparse set of light field views

Xiaoran Jiang (1) , Mikaël Le Pendu (1) , Christine Guillemot (1)

1 Sirocco - Analysis representation, compression and communication of visual data

This paper addresses the problem of depth estimation for every viewpoint of a dense light field, exploiting information from only a sparse set of views. This problem is particularly relevant for applications such as light field reconstruction from a subset of views, for view synthesis and for compression. Unlike most existing methods for scene depth estimation from light fields, the proposed algorithm computes disparity (or equivalently depth) for every viewpoint taking into account occlusions. In addition, it preserves the continuity of the depth space and does not require prior knowledge on the depth range. The experiments show that, both for synthetic and real light fields, our algorithm achieves competitive performance to state-of-the-art algorithms which exploit the entire light field and usually generate the depth map for the center viewpoint only.

Mots clés: stereo matching; light field; depth estimation; optical flow; low rank approximation

Domaines: Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Xiaoran Jiang, Mikaël Le Pendu, Christine Guillemot. Depth estimation with occlusion handling from a sparse set of light field views. ICIP 2018 - IEEE International Conference on Image Processing, Oct 2018, Athens, Greece. pp.634-638, ⟨10.1109/ICIP.2018.8451466⟩. ⟨hal-01786049v2⟩