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. Code is available at \url{https://github.com/justimyhxu/Dense-RepPoints}.
@article{arxiv.1912.11473,
title = {Dense RepPoints: Representing Visual Objects with Dense Point Sets},
author = {Ze Yang and Yinghao Xu and Han Xue and Zheng Zhang and Raquel Urtasun and Liwei Wang and Stephen Lin and Han Hu},
journal= {arXiv preprint arXiv:1912.11473},
year = {2020}
}