Method

LeAD-M3D: Leveraging Asymmetric Distillation for Real-time Monocular 3D Detection [LeAD-M3D]
[Anonymous Submission]

Submitted on 9 Nov. 2025 15:13 by
[Anonymous Submission]

Running time:0.0079 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Runtime with TensorRT: 7.9ms
Runtime without TensorRT: 23.6ms
Parameters:
Will be reported later
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) 93.45 % 91.53 % 84.33 %
Car (Orientation) 93.30 % 91.20 % 83.91 %
Car (3D Detection) 30.76 % 21.21 % 18.75 %
Car (Bird's Eye View) 38.36 % 26.58 % 23.74 %
Pedestrian (Detection) 78.10 % 61.53 % 56.86 %
Pedestrian (Orientation) 72.60 % 56.30 % 51.61 %
Pedestrian (3D Detection) 16.68 % 10.98 % 9.21 %
Pedestrian (Bird's Eye View) 19.06 % 12.52 % 10.76 %
Cyclist (Detection) 66.21 % 46.54 % 41.04 %
Cyclist (Orientation) 55.01 % 39.08 % 34.69 %
Cyclist (3D Detection) 9.28 % 5.47 % 4.70 %
Cyclist (Bird's Eye View) 12.20 % 7.15 % 6.14 %
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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