Dense RepPoints: Representing Visual Objects with Dense Point Sets

We present a new object representation, called Dense Rep-Points, which utilize a large number of points to describe the multi-grainedobject representation of both box level and pixel level. Techniques are pro-posed to efficiently process these dense points, which maintains nearconstant complexity with increasing point number. The dense RepPointsis proved to represent and learn object segment well, by a novel dis-tance transform sampling method combined with a set-to-set supervision.The novel distance transform sampling method combines the strengthof contour and grid representation, which significantly outperforms thecounter-parts using contour or grid representations. On COCO, it achieves39.6 mask AP and 48.3 bbox AP.