Method

Instance Diversity-Enriched Active Learning for Monocular 3D Detection [IDEAL-M3D 60%]
[Anonymous Submission]

Submitted on 15 May. 2025 22:40 by
[Anonymous Submission]

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

Method Description:
We propose IDEAL-M3D, the first instance-based active
learning pipeline for 3D Monocular Detection. Our method,
designed on diversity and efficiency principles, identifies
only the relevant objects to annotate. Our method is based on
thorough analysis and evaluation, which led us to also
address issues often overlooked by Monocular 3D Detection
methods, such as class imbalances.
Parameters:
Baseline: MonoLSS
Data: 60% of the trainval instances
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) 96.32 % 93.51 % 85.98 %
Car (Orientation) 96.22 % 93.27 % 85.51 %
Car (3D Detection) 27.06 % 18.87 % 16.73 %
Car (Bird's Eye View) 35.33 % 25.44 % 22.25 %
Pedestrian (Detection) 83.73 % 68.50 % 63.35 %
Pedestrian (Orientation) 75.99 % 61.20 % 56.16 %
Pedestrian (3D Detection) 13.73 % 8.50 % 7.52 %
Pedestrian (Bird's Eye View) 16.74 % 10.65 % 8.83 %
Cyclist (Detection) 75.87 % 58.75 % 50.33 %
Cyclist (Orientation) 68.10 % 50.82 % 43.58 %
Cyclist (3D Detection) 6.93 % 4.12 % 3.71 %
Cyclist (Bird's Eye View) 8.85 % 5.39 % 4.87 %
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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