Method

Monocular Tailored BEV and Residual Knowledge Distillation [MonoTRKDv2]


Submitted on 5 Nov. 2023 22:01 by
Christine Wu (University of Washington)

Running time:40 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We design a camera-aware Teacher and a target-aware
student, aiming to narrow the feature domain gap
between different modalities.
Parameters:
alpha=0.2, k=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) 93.78 % 80.76 % 75.36 %
Car (Orientation) 93.57 % 80.12 % 74.40 %
Car (3D Detection) 30.26 % 21.87 % 18.87 %
Car (Bird's Eye View) 46.87 % 36.89 % 33.39 %
Pedestrian (Detection) 59.66 % 44.54 % 40.12 %
Pedestrian (Orientation) 35.60 % 25.80 % 23.11 %
Pedestrian (3D Detection) 9.10 % 5.82 % 4.96 %
Pedestrian (Bird's Eye View) 11.05 % 7.22 % 6.11 %
Cyclist (Detection) 56.76 % 39.61 % 34.50 %
Cyclist (Orientation) 35.75 % 24.38 % 21.06 %
Cyclist (3D Detection) 8.01 % 4.44 % 3.68 %
Cyclist (Bird's Eye View) 9.08 % 5.01 % 4.21 %
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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