We present AVOD, an Aggregate View Object Detection network for autonomous
driving scenarios. The proposed neural network architecture uses LIDAR point
clouds and RGB images to generate features that are shared by two subnetworks:
a region proposal network (RPN) and a second stage detector network. The
proposed RPN uses a novel architecture capable of performing multimodal feature
fusion on high resolution feature maps to generate reliable 3D object proposals
for multiple object classes in road scenes. Using these proposals, the second
stage detection network performs accurate oriented 3D bounding box regression
and category classification to predict the extents, orientation, and
classification of objects in 3D space. Our proposed architecture is shown to
produce state of the art results on the KITTI 3D object detection benchmark
while running in real time with a low memory footprint, making it a suitable
candidate for deployment on autonomous vehicles. Code is at:
this https URL

AVOD 是一种用于自动驾驶场景的聚合视图目标检测神经网络，使用 LIDAR 点云和 RGB 图像生成特征来进行准确的 3D 目标检测及分类，实时低内存足迹的优秀表现使其成为自动驾驶部署的合适选择。