AI RESEARCH
SAM-Enhanced Segmentation on Road Datasets: Balancing Critical Classes in Autonomous Driving
arXiv CS.CV
•
ArXi:2605.28136v1 Announce Type: new Dense semantic segmentation is essential for autonomous driving, yet many multi-modal datasets lack pixel-level annotations. The Zenseact Open Dataset (ZOD) provides rich multi-sensor data but only bounding-box labels, limiting its use for segmentation research. Our primary contribution is a Segment Anything Model (SAM)-based annotation pipeline that produces dense, pixel-level annotations for ZOD by converting bounding boxes into semantic masks.