3D reconstruction of dynamic scenes with multiple handheld cameras
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
On the evaluation of scene flow estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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In this paper, we propose a novel dense depth recovery method for a trinocular video sequence. Specifically, we contribute a novel trinocular stereo matching model, which can effectively utilize the advantages of trinocular stereo images, and incorporate the visibility term with segmentation prior for robust depth estimate. In order to make the recovered depth maps more accurate and temporally consistent, we propose to first classify the pixels to static and dynamic ones, and then perform spatio-temporal depth optimization for them in different ways. Especially, we propose two motion models for handling dynamic pixels. The traditional bundle optimization model and our spatio-temporal optimization model are softly combined in a probabilistic way, so that the depths of both static and dynamic pixels can be effectively refined. Our automatic depth recovery approach is evaluated using a variety of challenging trinocular video sequences.