Method

LeAD-M3D: Leveraging Asymmetric Distillation for Real-time Monocular 3D Detection [LeAD-M3D]
https://github.com/deepscenario/LeAD-M3D-Inference

Submitted on 9 Nov. 2025 15:13 by
Johannes Meier (TU Munich)

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:
See paper
Latex Bibtex:
@inproceedings{meier2026leadm3d,
title = {{LeAD-M3D}: Leveraging Asymmetric
Distillation for Real-Time Monocular {3D}
Detection},
author = {Meier, Johannes and Michel,
Jonathan and Dhaouadi, Oussema and Yang, Yung-Hsu
and Reich, Christoph and Bauer,
Zuria and Roth, Stefan and Pollefeys, Marc
and Kaiser, Jacques and Cremers,
Daniel},
booktitle = {European Conference on Computer
Vision (ECCV)},
year = {2026},
}

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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