1,366 research outputs found
Joint 3D Proposal Generation and Object Detection from View Aggregation
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/avodComment: For any inquiries contact aharakeh(at)uwaterloo(dot)c
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