[CVPR2020/PaperSummary]Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

Figure1: Point Cloud Processing
Figure2- Point GNN architecture
Eq 1-Edge connectivity
Eq2:Vertex and Edge feature generation
Eq3: Vertex state information
Eq4: AutoRegistration offset and vertex state
Eq5: GNN single iteration equation
Eq6: Cross entropy formulae
Eq7: Encoding bounding box
Eq8: Localization loss
Eq9: Total loss formula
Eq10: Occlusion Factor
Table 1. The Average Precision (AP) comparison of 3D object detection on the KITTI test dataset
Table 2. The Average Precision (AP) comparison of Bird’s Eye View (BEV) object detection on the KITTI test dataset.
Table 3. Ablation study on the val. split of KITTI data
Figure3 : The blue dot indicates the original position of the vertices. The orange, purple, and red dots indicate the original position with added offsets from the first, the second, and the third graph neural network iterations. Best viewed in color.
Table 4. Average precision on the KITTI val. split using different
number of Point-GNN iterations.
Table 5. Average precision on downsampled KITTI val. split

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