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AVOD论文解析.ppt

发布时间：2022-06-19 发布人：admin 分类：说明书 资料大小：24.74M 资料格式：ppt 举报 版权申诉
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    AVOD Aggregate View Object Detection
    ONE Kitti Object Detection Dataset TWO AVOD paper THREE AVOD code
    Kitti Object Detection Dataset http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d
    Introduction data collecting platform: • • • • 2 × PointGray Flea2 grayscale cameras 2 × PointGray Flea2 color cameras 1 × Velodyne HDL-64E rotating 3D laser scanner 1 × OXTS RT3003 inertial and GPS navigation sy stem 4 × Edmund Optics lenses • tasks of interest : • stereo, optical flow, visual odometry, 3D object dete ction and 3D tracking. Ground truth: • Velodyne laser scanner and a GPS localization syst em.
    3D Object Detection Dataset class: • ’Van’, ’Car’, ’Truck’, ’Pedestrian’, ’Person (sitting)’, ’Cyclist’, ’Tram’ ,’Misc’ 3D bounding box overlap : • Car:70% • pedestrians 、cyclists:50% Difficulty: • Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 % • Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 % • Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %
    Point Cloud • N*4 matrics: (x,y,z, reflectivity) • Project to image coordinate: x = P2 * R0_rect * Tr_velo_to_cam * y
    Evaluation • Average Precision（AP）: • Average Orientation Similarity (AOS) :
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