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We introduce a new concept of 'co-recognition' for object-level image matching between an arbitrary image pair. Our method augments putative local region matches to reliable object-level correspondences without any supervision or prior knowledge on common objects. It provides the number of reliable common objects and the dense correspondences between the(More)
We address an unsupervised object detection and segmentation problem that goes beyond the conventional assumptions of one-to-one object correspondences or modeltest settings between images. Our method can detect and segment identical objects directly from a single image or a handful of images without any supervision. To detect and segment all the(More)
In this paper, we present a new framework for three-dimensional (3D) reconstruction of multiple rigid objects from dynamic scenes. Conventional 3D reconstruction from multiple views is applicable to static scenes, in which the configuration of objects is fixed while the images are taken. In our framework, we aim to reconstruct the 3D models of multiple(More)
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