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}.
翻译:我们提出了一个新的物体表示法,称为 " 登塞Reppoints ",它利用大量分数来描述一个多层次的物体,包括箱级和像素级。建议采用技术来高效处理这些密集点,保持接近连续的复杂度,并增加点数。显示登塞Repepoints能够很好地代表并学习物体部分,同时使用新的距离转换采样方法,同时采用定置到定位的监督。远程转换采样结合了等距和网格代表法的优势,导致性能超过基于等距或网格的对等点。代码可在以下https://github.com/justimyhxu/Dense-Reppoints}查阅。