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arXiv · 2007.07437

ContourRend: A Segmentation Method for Improving Contours by Rendering

Junwen Chen·Yi Lu·Yaran Chen·Dongbin Zhao·Zhonghua Pang
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Abstract

A good object segmentation should contain clear contours and complete regions. However, mask-based segmentation can not handle contour features well on a coarse prediction grid, thus causing problems of blurry edges. While contour-based segmentation provides contours directly, but misses contours' details. In order to obtain fine contours, we propose a segmentation method named ContourRend which adopts a contour renderer to refine segmentation contours. And we implement our method on a segmentation model based on graph convolutional network (GCN). For the single object segmentation task on cityscapes dataset, the GCN-based segmentation con-tour is used to generate a contour of a single object, then our contour renderer focuses on the pixels around the contour and predicts the category at high resolution. By rendering the contour result, our method reaches 72.41% mean intersection over union (IoU) and surpasses baseline Polygon-GCN by 1.22%.

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Junwen Chen, Yi Lu, Yaran Chen, Dongbin Zhao, Zhonghua Pang. 2020-07-15. ContourRend: A Segmentation Method for Improving Contours by Rendering. https://arxiv.org/abs/2007.07437

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