Learned Multimodal Compression for Autonomous Driving
Autonomous driving sensors generate an enormous amount of data. In this paper, we explore learned multimodal compression for autonomous driving, specifically targeted at 3D object detection. We focus on camera and LiDAR modalities and explore several coding approaches. One approach involves joint coding of fused modalities, while others involve coding one modality first, followed by conditional coding of the other modality. We evaluate the performance of these coding schemes on the nuScenes dataset. Our experimental results indicate that joint coding of fused modalities yields better results compared to the alternatives.
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3D Object DetectionAutonomous Drivingobject-detectionObject DetectionMethods 이 논문이 사용한 방법론
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