Method

Cross one feature 3D detection [COF3D]
[Anonymous Submission]

Submitted on 19 Aug. 2021 15:03 by
[Anonymous Submission]

Running time:200 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
We design an End-to-end Monocular 3D object
detection model. Our model is a ont-shot
framework, included the backbone, encoder and
detection head. For the backbone, we used both rgb
image and depth image, where depth image was
predicted by mono depth predict model
DORN(CVPR2018). we design the crossfeature moudle
to fuse the features. For the encoder, we design
the dilated encoder to gain the multi-scale
feature.
Parameters:
none
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) 87.85 % 80.16 % 61.97 %
Car (Orientation) 87.17 % 77.98 % 60.10 %
Car (3D Detection) 17.86 % 10.91 % 8.20 %
Car (Bird's Eye View) 25.36 % 15.39 % 11.34 %
Pedestrian (Detection) 50.45 % 35.34 % 31.28 %
Pedestrian (Orientation) 40.03 % 27.90 % 24.51 %
Pedestrian (3D Detection) 6.02 % 4.37 % 3.55 %
Pedestrian (Bird's Eye View) 7.20 % 4.78 % 4.55 %
Cyclist (Detection) 51.77 % 32.97 % 28.26 %
Cyclist (Orientation) 38.76 % 23.75 % 20.43 %
Cyclist (3D Detection) 2.34 % 1.46 % 1.28 %
Cyclist (Bird's Eye View) 2.70 % 1.60 % 1.55 %
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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