Method

Monocular 3D detection network based on global large kernel shallow network [MonoGLS]
[Anonymous Submission]

Submitted on 11 May. 2025 15:59 by
[Anonymous Submission]

Running time:0.04 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Monocular 3D detection network based on global
large kernel shallow network.To better acquire 3D
properties while reducing the computational
consumption of the model, this paper introduces an
innovative approach for monocular 3D object
detection.
Parameters:
batch_size: 16
num_workers: 16
class_merging: False
use_dontcare: False
use_3d_center: True
writelist: ['Car','Pedestrian','Cyclist']
random_flip: 0.5
random_crop: 0.5
scale: 0.4
shift: 0.1
drop_last_val: True
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) 96.15 % 93.32 % 85.74 %
Car (Orientation) 96.05 % 93.00 % 85.15 %
Car (3D Detection) 27.34 % 19.51 % 17.26 %
Car (Bird's Eye View) 37.13 % 26.72 % 23.10 %
Pedestrian (Detection) 83.64 % 66.47 % 61.46 %
Pedestrian (Orientation) 73.49 % 57.58 % 52.85 %
Pedestrian (3D Detection) 15.90 % 10.32 % 8.67 %
Pedestrian (Bird's Eye View) 16.46 % 10.79 % 9.67 %
Cyclist (Detection) 72.73 % 56.80 % 50.30 %
Cyclist (Orientation) 61.55 % 46.36 % 41.25 %
Cyclist (3D Detection) 8.75 % 5.37 % 4.67 %
Cyclist (Bird's Eye View) 8.99 % 6.05 % 5.33 %
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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