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What is: PointRend?

SourcePointRend: Image Segmentation as Rendering
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

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(x_i,y_i)f\left(x\_{i}, y\_{i}\right) that are defined over regular grids, and outputs high-resolution predictions p(x_i,y_i)p\left(x^{'}\_{i}, y^{'}\_{i}\right) 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 ff, and uses a small point head subnetwork to predict output labels from the point-wise features.