Feature Compression for Cloud-Edge Multimodal 3D Object Detection

This paper presents a method for compressing visual data in multimodal 3D object detection systems (systems that use multiple types of sensors like cameras and LiDAR to identify and locate objects in 3D space) when processing happens across both edge devices (local computers) and cloud servers. The authors propose two compression approaches: T-FFC (Transmission-Friendly Feature Compression), which reduces data size by 4933 times with minimal accuracy loss, and A-FFC (Accuracy-Friendly Feature Compression), which reduces data by 733 times with almost no accuracy loss, allowing cloud and edge devices to work together more efficiently.