3D object detection classifies the object category and estimates oriented 3D bounding boxes of physical objects from 3D sensor data.
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Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications.
In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline.