Cross-view Transformers for real-time Map-view Semantic Segmentation

Understand

We present cross-view transformers, an efficient attention-based model for map-view semantic segmentation from multiple cameras.

• Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism.
• Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration.
• These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically.