Method

Point-GNN [la] [Point-GNN]
https://github.com/WeijingShi/Point-GNN

Submitted on 12 Nov. 2019 07:47 by
Weijing Shi (CMU)

Running time:0.6 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
1070ti
Parameters:
1070ti
Latex Bibtex:
@misc{shi2020pointgnn,
title={Point-GNN: Graph Neural Network for 3D
Object Detection in a Point Cloud},
author={Weijing Shi and Ragunathan (Raj)
Rajkumar},
year={2020},
booktitle = {CVPR}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 96.58 % 93.50 % 88.35 %
Car (Orientation) 38.66 % 37.20 % 36.29 %
Car (3D Detection) 88.33 % 79.47 % 72.29 %
Car (Bird's Eye View) 93.11 % 89.17 % 83.90 %
Pedestrian (Detection) 71.59 % 58.20 % 54.06 %
Pedestrian (Orientation) 39.16 % 31.86 % 29.65 %
Pedestrian (3D Detection) 51.92 % 43.77 % 40.14 %
Pedestrian (Bird's Eye View) 55.36 % 47.07 % 44.61 %
Cyclist (Detection) 85.75 % 75.08 % 68.69 %
Cyclist (Orientation) 36.29 % 32.37 % 29.81 %
Cyclist (3D Detection) 78.60 % 63.48 % 57.08 %
Cyclist (Bird's Eye View) 81.17 % 67.28 % 59.67 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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