用显式投影预测提升船只定位精度
Improved Vision-to-Chart Buoy Association with Learned World-to-Image Projection
- 通过MLP直接预测浮标水线位置，替代隐式几何推理
- 测试集上整体得分0.7386，F1达0.8055，mIoU为0.6718
- 适合做视觉-地图关联任务的轻量级改进方案
本文针对MaCVi 2026视觉到图表关联挑战，对基于DETR的融合变压器基线进行轻量化改进。挑战基线解码器使用编码世界空间距离与方位的浮标查询，迫使变压器隐式学习从世界坐标到图像像素的复杂投影。本文提出训练一个专用MLP（QueryMLP），直接根据海图测量值和IMU姿态数据预测浮标在图像中的水线接触点。将预测的像素坐标附加至基线解码器查询向量中，为每个浮标提供直接的空间先验，减轻解码器的几何推理负担。在挑战排行榜上，该方法在保留测试集上取得总体得分0.7386，F1=0.8055，mIoU=0.6718，位列所有提交结果第二。
原文摘要 · Abstract (English)
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.
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