Method

we will update after publication [Res3DNet]


Submitted on 13 Jun. 2024 18:04 by
Ram Prasad Padhy (IIITDM)

Running time:0.05 s
Environment:GPU @ 3.5 Ghz (Python)

Method Description:
a simple and efficient 3D object detection using
LiDAR data.(more information after publication)
Parameters:
(more information after publication)
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) 95.44 % 94.05 % 91.32 %
Car (Orientation) 95.43 % 93.95 % 91.15 %
Car (3D Detection) 87.22 % 78.54 % 74.36 %
Car (Bird's Eye View) 91.71 % 88.16 % 84.85 %
Cyclist (Detection) 88.67 % 74.82 % 68.19 %
Cyclist (Orientation) 88.38 % 74.52 % 67.89 %
Cyclist (3D Detection) 76.11 % 60.47 % 53.77 %
Cyclist (Bird's Eye View) 79.47 % 64.64 % 57.99 %
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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