Dense RepPoints: Representing Visual Objects with Dense Point Sets - Yang, Ze; Xu, Yinghao; Xue, Han; Zhang, Zheng; Urtasun, Raquel; Wang, Liwei; Lin, Steve; Hu, Han (European Conference on Computer Vision (ECCV), 2020)
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level. Techniques are proposed to efficiently process these dense points, maintaining near-constant complexity with increasing point numbers. Dense RepPoints is shown to represent and learn object segments well, with the use of a novel distance transform sampling method combined with set-to-set supervision. The distance transform sampling combines the strengths of contour and grid representations, leading to performance that surpasses counterparts based on contours or grids.