Method

Stereo 3D object detection via prior depth information and adaptive spatial feature aggregation [SOD]
[Anonymous Submission]

Submitted on 25 Jan. 2021 02:03 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
we present a novel and high-performance framework for 3D object detection from stereo images,which combine stereo matching for accurate instance depth estimation and 3D object detection effectively.
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) 94.24 % 81.18 % 66.44 %
Car (Orientation) 94.15 % 80.62 % 65.94 %
Car (3D Detection) 70.90 % 48.69 % 40.12 %
Car (Bird's Eye View) 81.25 % 58.50 % 49.08 %
Pedestrian (Detection) 46.61 % 29.47 % 26.97 %
Pedestrian (Orientation) 33.28 % 21.00 % 19.08 %
Pedestrian (3D Detection) 21.13 % 14.68 % 12.67 %
Pedestrian (Bird's Eye View) 23.56 % 15.49 % 13.38 %
Cyclist (Detection) 60.07 % 40.95 % 34.02 %
Cyclist (Orientation) 50.69 % 32.81 % 27.45 %
Cyclist (3D Detection) 40.51 % 25.29 % 21.32 %
Cyclist (Bird's Eye View) 44.90 % 28.81 % 24.37 %
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.
This figure as: png eps pdf txt gnuplot




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