Papers › PointRend: Image Segmentation as Rendering
PointRend: Image Segmentation as Rendering
Alexander Kirillov, Yuxin Wu, Kaiming He, Ross Girshick
We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend.
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Code
Syntology Ran 5 of 18 code samples harvested from 3 repositories linked to this paper; 13 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 2 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | COCO 2017 val | PointRend (MaskR-CNN, ResNet-50-FPN) | mask AP* | 39.7 | #3 of 4 | Archive leaderboard | report |
| Instance Segmentation | Cityscapes val | PointRend | mask AP | 35.8 | #17 of 17 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | SemanticFPN P2-P5 + PointRend | mIoU | 78.6 | #60 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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