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Are Multi-view Edges Incomplete?


Depth reconstruction tries to obtain 3D scene geometry from incomplete or low-dimensional data — and it is usually a vital first step in many computational photography tasks. Most image-space geometric representations, however, fail to be general-purpose as they focus on a narrow set of metrics, and do not preserve all information of potential relevance. This dissertation shows that multi-view edges encode all relevant information for supporting higher level computational photography tasks that rely on depth reconstruction. We do this by presenting a novel encoding of multi-view scene geometry from structured light fields, and a reconstruction method for inverting this code. Our model is based on edges in the Epipolar Plane Images (EPIs) of a light field. These edges provide a small number of high-gradient key points and depth labels that can be used to accurately identify occlusion boundaries, and also to anchor the reconstruction in the angular domain for view-consistency. We present a differentiable representation of our model which allows the reconstruction to be optimized via gradient descent on a multi-view reconstruction loss. We evaluate our reconstruction for accuracy, view consistency, and occlusion handling to show that it retains all the geometric information required for higher level computational photography tasks.
Thesis (Ph. D.)--Brown University, 2022


Khan, Numair, "Are Multi-view Edges Incomplete?" (2022). Computer Science Theses and Dissertations. Brown Digital Repository. Brown University Library.