Dense RepPoints: Representing Visual Objects with Dense Point Sets
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We present an object representation, called Dense RepPoints, for flexible and detailed modeling of object appearance and geometry. In contrast to the coarse geometric localization and feature extraction of bounding boxes, Dense RepPoints adaptively distributes a dense set of points to semantically and geometrically significant positions on an object, providing informative cues for object analysis. Techniques are developed to address challenges related to supervised training for dense point sets from image segments annotations and making this extensive representation computationally practical. In addition, the versatility of this representation is exploited to model object structure over multiple levels of granularity. Dense RepPoints significantly improves performance on geometrically-oriented visual understanding tasks, including a 1.6 AP gain in object detection on the challenging COCO benchmark.
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