  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
inference, the inference speed will not be influenced.
  Experiments show the proposed ContourRender outperforms all the contour-based
instance segmentation approaches on COCO, while stays competitive with the
iteration-based state-of-the-art on Cityscapes. In addition, we specifically
select a subset from COCO val2017 named COCO ContourHard-val to further
demonstrate the contour quality improvements. Codes, models, and dataset split
will be released.
