PointRend is a novel method for efficient and high-quality image segmentation, inspired by classical computer graphics techniques. By treating image segmentation as a rendering problem, PointRend uses a point-based feature representation and a subdivision algorithm to adaptively select points for prediction, improving the accuracy and detail of object boundaries. The method can be integrated into existing models for instance and semantic segmentation tasks, achieving significant improvements on datasets like COCO and Cityscapes. PointRend's efficiency allows for high-resolution outputs with reduced computational and memory requirements compared to traditional methods. The paper presents a detailed implementation and evaluates PointRend's performance, demonstrating its effectiveness in both qualitative and quantitative metrics.PointRend is a novel method for efficient and high-quality image segmentation, inspired by classical computer graphics techniques. By treating image segmentation as a rendering problem, PointRend uses a point-based feature representation and a subdivision algorithm to adaptively select points for prediction, improving the accuracy and detail of object boundaries. The method can be integrated into existing models for instance and semantic segmentation tasks, achieving significant improvements on datasets like COCO and Cityscapes. PointRend's efficiency allows for high-resolution outputs with reduced computational and memory requirements compared to traditional methods. The paper presents a detailed implementation and evaluates PointRend's performance, demonstrating its effectiveness in both qualitative and quantitative metrics.