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.
Z. Yang, Y. Xu and H. Xue—Equal contribution.
This work was done when Ze Yang, Yinghao Xu and Han Xue were interns at Microsoft Research Asia.
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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.
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Yang, Z. et al. (2020). Dense RepPoints: Representing Visual Objects with Dense Point Sets. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12366. Springer, Cham. https://doi.org/10.1007/978-3-030-58589-1_14
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