Efficient Dense Depth Estimation from Dense Multiperspective Panoramas

In this paper we study how to compute a dense depth map with panoramic field of view (e.g., degrees) from multiperspective panoramas. A dense sequence of multiperspective panoramas is used for better accuracy and reduced ambiguity by taking advantage of significant data redundancy. To speed up the reconstruction, we derive an approximate epipolar plane image that is associated with the planar sweeping camera setup, and use one-dimensional window for efficient matching. To address the aperture problem introduced by one-dimensional window matching, we keep a set of possible depth candidates from matching scores. These candidates are then passed to a novel two-pass tensor voting scheme to select the optimal depth. By propagating the continuity and uniqueness constraints noniteratively in the voting process, our method produces highquality reconstruction results even when significant occlusion is present. Experiments on challenging synthetic and real scenes demonstrate the effectiveness and efficacy of our method.

Efficient dense depth estimation from dense multiperspective panoramas

In this puper we study how to compute U dense depth map with punorumic jield oj ' view (e.g., 360 degrees) from multi-perspective punommus. A dense sequence of multiperspective punorumus is used fiw better uccurucy and reduced ambiguity by tuking udvuntuge oj ' signijicunt dutu redunduncy. To speed up the reconstruction, we derive un upproximute epipolar plune imuge thut is ussociuted with the planar sweeping cameru setup. und use one-dimensional window jor efficient mutching. To uddress the uperture problem introduced by one-dimensional window matching, we keep U set oj ' possible depth cundidutes,from mutching scores. These cundidutes ure then pussed to U novel rwo-pu.s.s tensor voting scheme to select the optimal depth. By propuguting the continuity und uniqueness construints non-iterutively in the voting process, our method produces high-quulity reconstruction results even when signiJlcunt occlusion is present. Experiments on chullenging sjnthetic und real scenes demonstrutr the eflectiveness und eflcucy oj'our method.