We present a new object representation, called Dense RepPoints, which utilize a large number of points to describe the multi-grained object representation of both box level and pixel level. Techniques are proposed to efficiently process these dense points, which maintains near constant complexity with increasing point number. The Dense RepPoints is proved to represent and learn object segment well, by a novel distance transform sampling method combined with a set-to-set supervision. The novel distance transform sampling method combines the strength of contour and grid representation, which outperforms the counter-parts using contour or grid representations. Code is available at \url{https://github.com/justimyhxu/Dense-RepPoints}.

翻译：我们提出了一个新的物体表示法,称为Dense Reppoints,它使用许多点来描述框层和像素层的多重物体表示法,建议技术高效率地处理这些密集点,这些点的复杂程度几乎保持不变,而且点数不断增长。事实证明,Dense Reppoints通过一种新的远距离转换取样法,加上一个定置到定置的监督,能够很好地代表和学习物体段。新的距离转换取样法结合了等距和网格代表法的强度,后者利用等距或网格代表法比对面部分的表示法都强。代码可以在\url{https://github.com/justimyhxu/Dense-Reppoints}查阅。