ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass
Tutian Tang, Wenqiang Xu, Ruolin Ye, Yan-Feng Wang, Cewu Lu

TL;DR
ContourRender introduces a novel differentiable rendering approach for one-pass arbitrary contour shape detection in instance segmentation, improving contour quality without iterative refinement and maintaining high inference speed.
Contribution
It proposes ContourRender, a shape signature-based, differentiable rendering method that enhances contour prediction accuracy in one pass, surpassing previous contour-based methods.
Findings
Outperforms all contour-based methods on COCO
Competitive with iteration-based state-of-the-art on Cityscapes
Improves contour quality on COCO ContourHard-val
Abstract
Direct contour regression for instance segmentation is a challenging task. Previous works usually achieve it by learning to progressively refine the contour prediction or adopting a shape representation with limited expressiveness. In this work, we argue that the difficulty in regressing the contour points in one pass is mainly due to the ambiguity when discretizing a smooth contour into a polygon. To address the ambiguity, we propose a novel differentiable rendering-based approach named \textbf{ContourRender}. During training, it first predicts a contour generated by an invertible shape signature, and then optimizes the contour with the more stable silhouette by converting it to a contour mesh and rendering the mesh to a 2D map. This method significantly improves the quality of contour without iterations or cascaded refinements. Moreover, as optimization is not needed during…
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Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · Image and Object Detection Techniques · Infrastructure Maintenance and Monitoring
