Method

Hybrid 3D Object Detection [HDet3D]
[Anonymous Submission]

Submitted on 19 Dec. 2023 08:13 by
[Anonymous Submission]

Running time:0.07 s
Environment:>8 cores @ 2.5 Ghz (Python)

Method Description:
We propose a hybrid method which utilizes both RGB
and Point cloud data. At one end, we propose a 2D
pillar network to process the RGB data. At the other
end, we propose a sparse 3DCNN network to process 3D
point cloud.
Parameters:
OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
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) 96.69 % 96.12 % 91.01 %
Car (Orientation) 96.69 % 96.00 % 90.84 %
Car (3D Detection) 89.93 % 82.33 % 77.20 %
Car (Bird's Eye View) 94.90 % 91.82 % 84.68 %
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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