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.
In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.
SOTA for 3D Object Detection on nuScenes
Current 3D object detection methods are heavily influenced by 2D detectors.
SOTA for 3D Object Detection on SUN-RGBD
Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.
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.
We introduce a novel method for 3D object detection and pose estimation from color images only.
#6 best model for 6D Pose Estimation using RGB on LineMOD