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Zenseact

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Representative Papers

Predicting Signed Distance Functions for Visual Instance Segmentation

Aug 13, 2026

This work addresses the challenge of accurately modeling highly variable or elongated objects—such as ropes—in instance segmentation, where conventional anchor-based methods often fail to capture complex shapes. To overcome this limitation, the authors propose a novel anchor-free paradigm that leverages neural networks to predict, at each pixel, multi-directional distances to the nearest object boundary. These predictions are aggregated to approximate a signed distance function (SDF), from which a foreground mask is obtained via thresholding. By performing pixel-wise directional distance regression, the method flexibly represents arbitrary object geometries without relying on predefined anchors. Evaluated on the COCO dataset, the approach achieves superior segmentation IoU compared to state-of-the-art methods like YOLACT, demonstrating significantly enhanced adaptability to irregular object shapes.

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Latest Papers

Predicting Signed Distance Functions for Visual Instance Segmentation

Aug 13, 2026

This work addresses the challenge of accurately modeling highly variable or elongated objects—such as ropes—in instance segmentation, where conventional anchor-based methods often fail to capture complex shapes. To overcome this limitation, the authors propose a novel anchor-free paradigm that leverages neural networks to predict, at each pixel, multi-directional distances to the nearest object boundary. These predictions are aggregated to approximate a signed distance function (SDF), from which a foreground mask is obtained via thresholding. By performing pixel-wise directional distance regression, the method flexibly represents arbitrary object geometries without relying on predefined anchors. Evaluated on the COCO dataset, the approach achieves superior segmentation IoU compared to state-of-the-art methods like YOLACT, demonstrating significantly enhanced adaptability to irregular object shapes.

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