Cross-view Transformers for real-time Map-view Semantic Segmentation (2205.02833v1)

Abstract: 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.

• The paper introduces a cross-view transformer that learns map-view segmentation from multi-camera inputs without explicit geometric modeling.
• The model leverages camera-aware positional embeddings and attention mechanisms to combine convolutional features, achieving real-time performance at 35 FPS.
• By attaining state-of-the-art results on the nuScenes dataset with efficient training, it offers promising directions for autonomous vehicle applications.

Overview of Cross-view Transformers for Real-time Map-view Semantic Segmentation

The paper "Cross-view Transformers for Real-time Map-view Semantic Segmentation" by Brady Zhou and Philipp Kr{\"a}henb{\"u}hl introduces a novel approach to map-view semantic segmentation utilizing cross-view transformers. This work explores efficiently mapping multiple camera inputs into a unified map-view representation, crucial for autonomous vehicle navigation.

Methodology

The core innovation of the paper lies in using a cross-view transformer architecture that departs from explicit geometric modeling. Instead, it leverages a camera-aware cross-view attention mechanism underpinned by a sophisticated positional embedding scheme. These embeddings are derived from the intrinsic and extrinsic calibrations of each camera, enabling the model to learn transformations from camera views to a map-view without direct geometric reasoning.

The architecture is composed of a convolutional image encoder, which processes each camera view to extract features, and cross-view transformer layers that execute the map-view semantic segmentation. Attention mechanisms in the transformer layer facilitate the mapping by focusing on relevant features across different views, guided by camera-aware and map-view positional embeddings.

Numerical Results

The proposed model demonstrates significant efficiency and performance benefits. It achieves state-of-the-art results on the nuScenes dataset, handling vehicle and road segmentation tasks. The model operates at a rapid inference speed of 35 FPS on an RTX 2080 Ti GPU, making it suitable for real-time applications. Its training process is also efficient, requiring only 32 GPU hours.

Implications and Future Directions

The implications of this research extend to both practical and theoretical domains. Practically, the simplicity and speed of the model make it an attractive option for real-time autonomous vehicle applications. Theoretically, the use of attention mechanisms in lieu of explicit geometric modeling offers a promising direction for further exploration in scene understanding tasks.

Future developments could explore enhancing the transformer architecture or integrating additional data modalities, such as LiDAR or radar, to refine segmentation accuracy and extend applicability. The model’s ability to learn implicit geometric relationships presents opportunities for broader application in areas requiring robust multi-view fusion.

Conclusion

In summary, this paper contributes a methodologically innovative approach to multi-view semantic segmentation in autonomous systems. By demonstrating superior performance through implicit learning of geometric transformations, the cross-view transformer architecture represents a meaningful advancement in the efficient and effective processing of multi-view camera data.