Method

[la] ST-RCNN: 3D Object Detection Based on Scene Twins with Pretext Tasks [ST-RCNN]
[Anonymous Submission]

Submitted on 12 Sep. 2021 08:35 by
[Anonymous Submission]

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
We propose an innovative 3D object detector based on
scene twins, named ST-RCNN, to explore more abundant
information in finite point cloud.
Parameters:
\alpha = 0.3, \beta = 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) 98.06 % 94.79 % 92.12 %
Car (Orientation) 98.05 % 94.69 % 91.97 %
Car (3D Detection) 90.50 % 81.84 % 77.22 %
Car (Bird's Eye View) 94.58 % 90.53 % 86.08 %
Pedestrian (Detection) 64.50 % 55.46 % 52.89 %
Pedestrian (Orientation) 61.38 % 52.00 % 49.18 %
Pedestrian (3D Detection) 44.96 % 39.36 % 37.09 %
Pedestrian (Bird's Eye View) 49.78 % 44.14 % 41.95 %
Cyclist (Detection) 85.84 % 77.57 % 70.92 %
Cyclist (Orientation) 85.37 % 76.91 % 70.32 %
Cyclist (3D Detection) 78.82 % 65.61 % 58.44 %
Cyclist (Bird's Eye View) 80.69 % 69.42 % 62.63 %
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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