Salient object detection in SfM point cloud

National Conference on Computer Vision Pattern Recognition Image Processing and Graphics(2013)

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摘要
In this paper we present a max-flow min-cut based salient object detection in 3D point cloud that results from Structure from Motion (SfM) pipeline. The SfM pipeline generates noisy point cloud due to the unwanted scenes captured along with the object in the image dataset of SfM. The background points being sparse and not meaningful, it becomes necessary to remove them. Hence, any further processes (like surface reconstruction) utilizing the cleaned up model will have no hinderance from the noise removed. We present a novel approach where the camera centers are used to segment out the salient object. The algorithm is completely autonomous and does not need any user input. We test our proposed method on Indian historical models reconstructed through SfM. We evaluate the results in terms of selectivity and specificity.
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image segmentation
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