Event Cameras offer appealing advantages, including power efficiency and ultra-low latency, driving forward advancements in edge applications. In order to leverage mature frame-based algorithms, most approaches typically compute dense, image-like representations from sparse, asynchronous events. However, they are often unable to capture comprehensive information or are computationally intensive, which hinders the edge deployment of event-based vision. Meanwhile, pillar-based paradigms have been proven to be efficient and well established for dense representations of sparse data. Hence, from a novel pillar-based perspective, we present EventPillars, an efficient, comprehensive framework for dense event representations. To summarize, it (i) incorporates the Temporal Event Range to describe an intact temporal distribution, (ii) Activates the Event Polarities to explicitly record the scene dynamics, (iii) enhances the target awareness by a spatial attention prior from Normalized Event Density, (iv) can be plug-and-played into different downstream tasks. Extensive experiments show that our EventPillars records a new state-of-the-art precision on object recognition and detection datasets with surprisingly 9.2× and 4.5× lower computation and storage consumption. This brings a new insight into dense event representations and is promising to boost the edge deployment of event-based vision.

Current LiDAR-Camera fusion methods for 3D object detection achieve considerable accuracy at the immense cost of computation and storage, posing challenges for the deployment at the edge. To address this issue, we propose a lightweight 3D object detection framework, namely TinyFusionDet. Specially, we put forward an ingenious Hybrid Scale Pillar Strategy in LiDAR point cloud feature extraction to efficiently improve the detection accuracy of small objects. Meanwhile, a low cost Cross-Modal Heatmap Attention module is presented to suppress background interference in image features for reducing false positives. Moreover, a Cross-Modal Feature Interaction module is designed to enhance the cross-modal information fusion among channels for further promoting the detection precision. Extensive experiments demonstrated that TinyFusionDet achieves competitive accuracy with the lowest memory consumption and inference latency, making it suitable for hardware constrained edge devices. Furthermore, TinyFusionDet is implemented on a customized FPGA-based prototype system, yielding a record high energy efficiency up to 114.97GOPS/W. To the best of our knowledge, this marks the first real-time LiDAR-Camera fusion detection framework for edge applications.