# Dense RepPoints: Representing Visual Objects with Dense Point Sets

arXiv: https://arxiv.org/abs/1912.11473 · HTML: https://mlsanity.com/paper/1912.11473/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [Oriented RepPoints for Aerial Object Detection](https://mlsanity.com/paper/2105.11111/) (2021)
- [Sparse Instance Activation for Real-Time Instance Segmentation](https://mlsanity.com/paper/2203.12827/) (2022)
- [S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling](https://mlsanity.com/paper/2101.06571/) (2021)
- [All in Tokens: Unifying Output Space of Visual Tasks via Soft Token](https://mlsanity.com/paper/2301.02229/) (2023)
- [Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing](https://mlsanity.com/paper/2103.04570/) (2021)
- [E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation](https://mlsanity.com/paper/2203.04074/) (2022)
- [Towards Domain Generalization in Object Detection](https://mlsanity.com/paper/2203.14387/) (2022)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [PointScatter: Point Set Representation for Tubular Structure Extraction](https://mlsanity.com/paper/2209.05774/) (2022)
- [NAS-FCOS: Efficient Search for Object Detection Architectures](https://mlsanity.com/paper/2110.12423/) (2021)
- [Growing Instance Mask on Leaf](https://mlsanity.com/paper/2211.16738/) (2022)
- [FourierMask: Instance Segmentation using Fourier Mapping in Implicit Neural Networks](https://mlsanity.com/paper/2112.12535/) (2021)
- [Selective Multi-Scale Learning for Object Detection](https://mlsanity.com/paper/2206.08206/) (2022)
- [AdaContour: Adaptive Contour Descriptor with Hierarchical Representation](https://mlsanity.com/paper/2404.08292/) (2024)

## Abstract (arXiv)

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