Method

MonoHD [MonoHD]
[Anonymous Submission]

Submitted on 1 Nov. 2025 23:58 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Rethink Depth Awareness for Mono3D from a holistic
perspective.
Parameters:
none
Latex Bibtex:
none

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) 96.37 % 95.65 % 90.69 %
Car (Orientation) 96.32 % 95.52 % 90.47 %
Car (3D Detection) 33.42 % 23.73 % 20.76 %
Car (Bird's Eye View) 42.61 % 30.61 % 27.38 %
Pedestrian (Detection) 86.04 % 72.77 % 67.88 %
Pedestrian (Orientation) 83.81 % 69.90 % 64.90 %
Pedestrian (3D Detection) 10.94 % 7.67 % 7.06 %
Pedestrian (Bird's Eye View) 12.56 % 8.98 % 7.85 %
Cyclist (Detection) 84.42 % 66.64 % 59.86 %
Cyclist (Orientation) 82.06 % 63.55 % 56.81 %
Cyclist (3D Detection) 11.08 % 7.04 % 6.46 %
Cyclist (Bird's Eye View) 13.16 % 8.61 % 7.97 %
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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