Method

Interpretable Object Detection in Foggy Point Cloud [IFOD]


Submitted on 27 May. 2026 05:25 by
Jinbiao Zhao (hfut,china)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose
Interpretable Object Detection in Foggy Point
Cloud (IFOD),
a novel architecture that employs physically-
motivated design
principles through an Atmospheric Scattering Model
(ASM) to
achieve robust perception in foggy condition
Parameters:
null
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) 96.21 % 94.90 % 92.20 %
Car (Orientation) 96.20 % 94.80 % 92.01 %
Car (3D Detection) 88.37 % 81.51 % 76.87 %
Car (Bird's Eye View) 92.50 % 88.74 % 86.00 %
Pedestrian (Detection) 68.45 % 58.84 % 56.32 %
Pedestrian (Orientation) 63.98 % 53.96 % 51.04 %
Pedestrian (3D Detection) 49.88 % 42.60 % 39.51 %
Pedestrian (Bird's Eye View) 53.36 % 46.98 % 43.98 %
Cyclist (Detection) 87.17 % 73.88 % 66.85 %
Cyclist (Orientation) 87.03 % 73.65 % 66.61 %
Cyclist (3D Detection) 77.02 % 59.87 % 53.35 %
Cyclist (Bird's Eye View) 78.14 % 64.36 % 56.99 %
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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