Official code for the paper ActFormer: Scalable Collaborative Perception via Active Queries, and project website accepted by 2024 IEEE International Conference on Robotics and Automation. Currently the code is a raw version, will be updated ASAP. If you have any inquiries, feel free to contact [email protected]
Collaborative perception leverages rich visual observations from multiple robots to extend a single robot's perception ability beyond its field of view. Many prior works receive messages broadcast from all collaborators, leading to a scalability challenge when dealing with a large number of robots and sensors.
In this work, we aim to address scalable camera-based collaborative perception with a Transformer-based architecture. Our key idea is to enable a single robot to intelligently discern the relevance of the collaborators and their associated cameras according to a learned spatial prior. This proactive understanding of the visual features' relevance does not require the transmission of the features themselves, enhancing both communication and computation efficiency. Specifically, we present ActFormer, a Transformer that learns bird's eye view (BEV) representations by using predefined BEV queries to interact with multi-robot multi-camera inputs. Each BEV query can actively select relevant cameras for information aggregation based on pose information, instead of interacting with all cameras indiscriminately. Experiments on the V2X-Sim dataset demonstrate that ActFormer improves the detection performance from 29.89% to 45.15% in terms of [email protected] with about 50% fewer queries, showcasing the effectiveness of ActFormer in multi-agent collaborative 3D object detection.
same as BEVFormer
See details in Install requirements
The experiments are completed in nuscene file format.
Download V2X-Sim 2.0
See details in Data Access
See train, test and visual details in Start