Method

Combining KNN graph [KNN-GCNN]
[Anonymous Submission]

Submitted on 17 Mar. 2020 15:03 by
[Anonymous Submission]

Running time:0.4 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
Combining KNN graph convolution and voxel network
to
extract features
Combine image and lidar information
Parameters:
\alpha=0.2
Latex Bibtex:

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) 96.19 % 93.39 % 88.17 %
Car (Orientation) 38.80 % 37.80 % 36.52 %
Car (3D Detection) 86.37 % 78.26 % 71.14 %
Car (Bird's Eye View) 91.73 % 88.57 % 83.32 %
Pedestrian (Detection) 69.53 % 56.80 % 52.86 %
Pedestrian (Orientation) 39.25 % 31.91 % 29.76 %
Pedestrian (3D Detection) 51.82 % 43.57 % 40.02 %
Pedestrian (Bird's Eye View) 55.11 % 46.77 % 44.43 %
Cyclist (Detection) 88.83 % 76.52 % 69.82 %
Cyclist (Orientation) 39.32 % 34.03 % 31.17 %
Cyclist (3D Detection) 80.24 % 62.91 % 56.49 %
Cyclist (Bird's Eye View) 83.35 % 67.22 % 59.51 %
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.
This figure as: png eps pdf txt gnuplot



3D object detection results.
This figure as: png eps pdf txt gnuplot



Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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