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Zenseact

Industry researcheurope · se
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Research library21linked papers
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Selected work

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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SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning

Jul 01, 2026

This work addresses the challenge of achieving scalable and robust ego-vehicle trajectory prediction without reliance on high-definition maps. To this end, we propose an end-to-end system that integrates front-view images, vehicle kinematics, and navigation paths derived from standard-definition (SD) maps. We introduce SD map paths as a semantic prior for trajectory prediction for the first time, and design a dual-hypothesis fusion architecture with a gated classifier to handle challenges such as route corruption or visual ambiguity. Evaluated on 480,000 real-world driving scenarios spanning ten European countries and the United States, our method reduces the average displacement error (ADE) over an 8-second horizon by 16.9% compared to a baseline using only images and kinematics. We also release an open-source toolkit for SD path generation to support community benchmarking.

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Recent publications

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.

0 citationsRead paper

SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning

Jul 01, 2026

This work addresses the challenge of achieving scalable and robust ego-vehicle trajectory prediction without reliance on high-definition maps. To this end, we propose an end-to-end system that integrates front-view images, vehicle kinematics, and navigation paths derived from standard-definition (SD) maps. We introduce SD map paths as a semantic prior for trajectory prediction for the first time, and design a dual-hypothesis fusion architecture with a gated classifier to handle challenges such as route corruption or visual ambiguity. Evaluated on 480,000 real-world driving scenarios spanning ten European countries and the United States, our method reduces the average displacement error (ADE) over an 8-second horizon by 16.9% compared to a baseline using only images and kinematics. We also release an open-source toolkit for SD path generation to support community benchmarking.

0 citationsRead paper