XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation

ArXi:2601.14477v2 Announce Type: replace-cross Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor-specific data availability. To bridge the gap between available datasets and deployment domains, domain adaptation strategies are widely used. In this work, we propose XD-MAP, a novel approach to transfer sensor-specific knowledge from an image dataset to LiDAR, an entirely different sensing domain. Our method leverages detections on camera images to create a semantic parametric map.