Methods › Computer Vision › Semantic Segmentation Modules › PointRend

PointRend

9 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

PointRend is a module for image segmentation tasks, such as instance and semantic segmentation, that attempts to treat segmentation as image rending problem to efficiently "render" high-quality label maps. It uses a subdivision strategy to adaptively select a non-uniform set of points at which to compute labels. PointRend can be incorporated into popular meta-architectures for both instance segmentation (e.g. Mask R-CNN) and semantic segmentation (e.g. FCN). Its subdivision strategy efficiently computes high-resolution segmentation maps using an order of magnitude fewer floating-point operations than direct, dense computation.

PointRend is a general module that admits many possible implementations. Viewed abstractly, a PointRend module accepts one or more typical CNN feature maps f(xᵢ, yᵢ) that are defined over regular grids, and outputs high-resolution predictions p(x^′ᵢ, y^′ᵢ) over a finer grid. Instead of making excessive predictions over all points on the output grid, PointRend makes predictions only on carefully selected points. To make these predictions, it extracts a point-wise feature representation for the selected points by interpolating f, and uses a small point head subnetwork to predict output labels from the point-wise features.

Source: PointRend: Image Segmentation as Rendering

Papers archive 2025-07-28

9 shown of 9, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Instance Segmentation8
Segmentation8
Semantic Segmentation8
Image Segmentation3
Object2
Virtual Try-on1
Weakly-supervised instance segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with PointRend: 2019 to 2023, peak 4 4 0 2019: 1 paper 2019 2020: 2 papers 2020 2021: 2 papers 2021 2022: 0 papers 2022 2023: 4 papers 2023
Papers per year the archive tags with this method, by the paper's archive date (9 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semantic Segmentation Modules

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