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CrossFusion net : Deep 3D object detection based on RGB images and point clouds in autonomous driving (應用於自動駕駛基於點雲集圖片之3D物件偵測網路)
  • 發布年度:2020
  • 主要類別:車載資通訊
  • 次要類別:論文
  • Abstract

    In recent years, accurate 3D detection plays an important role in a lot of applications. Autonomous driving, for instance, is one of typical representatives. This paper aims to design an accurate 3D detector that takes both LiDAR point clouds and RGB images as inputs according to the fact. Lidat and camera have their own merits. A deep novel end-to end two-stream learnable architecture, CrossFusion Net, is designed to exploit reatures from both Lidar point clouds as well as RGB images through a hierarchical fusion structure. Specifically, CrossFusion Net utilizes bird's eye view (BEV) of point clouds through projection. Besides, these two feature maps of different streams are fused through the newly introduced CrossFusion (CF) layer.