  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}.
