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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
We propose a new technique for computing dense scene flow from two handheld
videos with wide camera baselines and different photometric properties due to
different sensors or camera settings like exposure and white balance. Our
technique innovates in two ways over existing methods: (1) it supports
independently moving cameras, and (2) it computes dense scene flow for
wide-baseline scenarios.We achieve this by combining state-of-the-art
wide-baseline correspondence finding with a variational scene flow formulation.
First, we compute dense, wide-baseline correspondences using DAISY descriptors
for matching between cameras and over time. We then detect and replace occluded
pixels in the correspondence fields using a novel edge-preserving Laplacian
correspondence completion technique. We finally refine the computed
correspondence fields in a variational scene flow formulation. We show dense
scene flow results computed from challenging datasets with independently
moving, handheld cameras of varying camera settings.
