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Dense RepPoints: Representing Visual Objects with Dense Point Sets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)

Abstract

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 https://github.com/justimyhxu/Dense-RepPoints.

Notes

Acknowledgement

We thank Jifeng Dai and Bolei Zhou for discussion and comments about this work. Jifeng Dai was involved in early discussions of the work and gave up authorship after he joined another company.

Supplementary material

504479_1_En_14_MOESM1_ESM.pdf (678 kb)
Supplementary material 1 (pdf 677 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Peking UniversityBeijingChina
  2. 2.Zhejiang LabHangzhouChina
  3. 3.Zhejiang UniversityHangzhouChina
  4. 4.The Chinese University of Hong KongHong KongChina
  5. 5.Shanghai Jiao Tong UniversityShanghaiChina
  6. 6.University of TorontoTorontoCanada
  7. 7.Microsoft Research AsiaBeijingChina

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