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InfoResearchPeer-reviewed
Feature Compression for Cloud-Edge Multimodal 3D Object Detection
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Summary
This paper proposes two feature compression modes for cloud-edge multimodal 3D object detection built on a sparse tensor-based network at the edge device. Transmission-Friendly Feature Compression (T-FFC) sends only the last backbone layer output and reaches a compression factor of 4933 on KITTI with the VirConv-L network, at under a 3% loss in detection performance. Accuracy-Friendly Feature Compression (A-FFC) adds two feature types, compressing by about 733 with almost no performance loss.