Method

Unsupervised 3D Object Detection by Commonsense Clue [CPD++(unsupervised)]
https://github.com/hailanyi/CPD

Submitted on 20 Dec. 2024 09:18 by
hai wu (xiamen university)

Running time:0.1 s
Environment:GPU @ >3.5 Ghz (Python)

Method Description:
Based on the CPD, we further propose CPD++, an
enhanced version that boosts performance by
motion clue. CPD++ learns localization from
stationary objects and learns recognition from
moving objects. It then facilitates the mutual
transfer of localization and recognition knowledge
between these two object types.
Parameters:
NAN
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) 94.96 % 86.95 % 83.72 %
Car (Orientation) 93.98 % 86.07 % 82.76 %
Car (3D Detection) 84.20 % 67.90 % 62.53 %
Car (Bird's Eye View) 90.22 % 81.62 % 76.56 %
Pedestrian (Detection) 39.25 % 32.46 % 30.79 %
Pedestrian (Orientation) 12.16 % 10.69 % 10.20 %
Pedestrian (3D Detection) 17.64 % 15.58 % 14.60 %
Pedestrian (Bird's Eye View) 28.36 % 24.43 % 23.22 %
Cyclist (Detection) 57.51 % 42.56 % 35.46 %
Cyclist (Orientation) 28.13 % 21.26 % 17.94 %
Cyclist (3D Detection) 50.49 % 33.84 % 29.35 %
Cyclist (Bird's Eye View) 56.35 % 36.97 % 32.33 %
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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