Joint 3D Proposal Generation and Object Detection from View Aggregation

2017-12-06 · Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, Steven Waslander

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: https://github.com/kujason/avod

📄 PDF Abstract BibTeX arXiv:1712.02294

Code (4)

kujason/avod 공식 구현 tf

Fredrik00/avod tf

asharakeh/kitti_native_evaluation pytorch

kujason/ip_basic

Tasks

3D Object Detection Autonomous Driving Autonomous Vehicles General ClassificationObjectobject-detection Object DetectionRegion Proposal

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