Method

Unity-drive innovation mono3D [UDI-mono3D]


Submitted on 14 Jul. 2020 05:14 by
yixiong liao (unity-drive innovations)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Our network generates 3D bounding box directly;There is a good balance between speed and precision;Meet the real-time requirements
Parameters:
only image as input
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) 93.93 % 88.16 % 79.57 %
Car (Orientation) 91.88 % 84.20 % 75.38 %
Car (3D Detection) 0.51 % 0.41 % 0.43 %
Car (Bird's Eye View) 3.38 % 2.79 % 2.37 %
Pedestrian (Detection) 77.16 % 62.26 % 56.39 %
Pedestrian (Orientation) 57.42 % 44.75 % 40.21 %
Pedestrian (3D Detection) 1.81 % 1.01 % 0.99 %
Pedestrian (Bird's Eye View) 2.09 % 1.42 % 1.07 %
Cyclist (Detection) 77.70 % 59.44 % 51.49 %
Cyclist (Orientation) 57.16 % 42.21 % 36.30 %
Cyclist (3D Detection) 3.29 % 1.74 % 1.37 %
Cyclist (Bird's Eye View) 3.59 % 2.01 % 1.79 %
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.
This figure as: png eps pdf txt gnuplot



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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