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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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.
Current 3D object detection methods are heavily influenced by 2D detectors.
SOTA for 3D Object Detection on SUN-RGBD val
This report presents our method which wins the nuScenes3D Detection Challenge  held in Workshop on Autonomous Driving(WAD, CVPR 2019).
SOTA for 3D Object Detection on nuScenes
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
#2 best model for Birds Eye View Object Detection on KITTI Pedestrians Moderate
However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference.
#2 best model for 3D object detection from stereo images on KITTI Cars Moderate
In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline.
#3 best model for Birds Eye View Object Detection on KITTI Cyclists Moderate