3D object detection classifies the object category and estimates oriented 3D bounding boxes of physical objects from 3D sensor data.
( Image credit: AVOD )
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In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline.
We introduce a novel method for 3D object detection and pose estimation from color images only.
Ranked #11 on 6D Pose Estimation using RGB on LineMOD
The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.
Ranked #2 on Multiple Object Tracking on KITTI Tracking test
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 proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
We encode the sparse 3D point cloud with a compact multi-view representation.