# Improved Vision-to-Chart Buoy Association with Learned World-to-Image Projection Source: [Improved Vision-to-Chart Buoy Association with Learned World-to-Image Projection]() Collection index: [llms.txt]() Data updated: 2026-05-26T05:00:05.667Z Authors: Borja Carrillo-Perez Published: 2026-05-21T18:17:55.000Z ## Abstract This report presents a lightweight modification to the DETR-based fusion transformer baseline for the MaCVi 2026 Vision-to-Chart data association challenge. The challenge baseline decoder receives per-buoy queries encoding world-space distance and bearing, forcing the transformer to implicitly learn the complex geometric projection from world coordinates to image pixels. Instead, this work trains an additional dedicated MLP, QueryMLP, to explicitly predict the buoy's waterline contact point in the image from chart measurements and IMU orientation data. The predicted pixel coordinates are appended to the baseline decoder query vector, providing a direct spatial prior per buoy and reducing the geometric reasoning burden on the transformer decoder. On the challenge leaderboard, the presented approach achieves an Overall score of 0.7386, with F1 = 0.8055 and mIoU = 0.6718, on the held-out test set, placing second among all submissions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Vision-to-Chart buoy association on ATON (val)](): Vision-to-Chart buoy association; ATON (val); Precision (P): 86.27. - [Vision-to-Chart buoy association on ATON (test)](): Vision-to-Chart buoy association; ATON (test); Precision: 85.63. ## Original sources and implementation [Original paper]() [PDF]()