Method

Sample-Aware Knowledge Distillation between Monocular Camera-based 3D Object Detectors with MonoRCNN [SAKD-MR-Res18]
[Anonymous Submission]

Submitted on 17 Aug. 2024 15:28 by
[Anonymous Submission]

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
MonoRCNN++ resnet18 model Knowledge distilled by
SAKD
Parameters:
region_aware weight = 1
class_hardness weight = 3
class_hardness alpha = 0.6
class_hardness beta = 1.5
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) 94.50 % 86.73 % 71.87 %
Car (Orientation) 94.42 % 86.35 % 71.42 %
Car (3D Detection) 18.38 % 11.65 % 9.42 %
Car (Bird's Eye View) 26.48 % 16.56 % 13.67 %
Pedestrian (Detection) 74.40 % 55.20 % 48.08 %
Pedestrian (Orientation) 67.47 % 49.57 % 43.12 %
Pedestrian (3D Detection) 12.62 % 8.06 % 6.78 %
Pedestrian (Bird's Eye View) 13.52 % 8.60 % 7.18 %
Cyclist (Detection) 64.63 % 45.72 % 39.60 %
Cyclist (Orientation) 52.25 % 36.74 % 32.07 %
Cyclist (3D Detection) 3.50 % 1.87 % 1.55 %
Cyclist (Bird's Eye View) 4.54 % 2.10 % 2.24 %
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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