Cross-view Transformers for real-time Map-view Semantic Segmentation
This addresses the problem of efficient and accurate semantic mapping for autonomous driving systems, representing an incremental improvement with specific gains in speed.
The paper tackles real-time map-view semantic segmentation from multiple cameras by introducing cross-view transformers, achieving state-of-the-art performance on the nuScenes dataset with 4x faster inference speeds.
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. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in real-time. The presented architecture performs at state-of-the-art on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://github.com/bradyz/cross_view_transformers.