Dinghuang Ji

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We target the sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of(More)
We propose a framework to automatically build 3D models for scenes containing structures not amenable for photo-consistency based reconstruction due to having dynamic appearance. We analyze the dynamic appearance elements of a given scene by leveraging the imagery contained in Internet image photo-collections and online video sharing websites. Our approach(More)
Internet photo collections naturally contain a large variety of illumination conditions, with the largest difference between day and night images. Current modeling techniques do not embrace the broad illumination range often leading to reconstruction failure or severe artifacts. We present an algorithm that leverages the appearance variety to obtain more(More)
We propose a framework for the automatic creation of time-lapse mosaics of a given scene. We achieve this by leveraging the illumination variations captured in Internet photo-collections. In order to depict and characterize the illumination spectrum of a scene, our method relies on building discrete representations of the image appearance space through(More)
—We target the problem of sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as(More)
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