Method

depth embedding [DE_Fusion]


Submitted on 18 Jun. 2023 09:48 by
Chaofeng Ji (Xi’an Jiaotong University)

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a method for monocular 3D object
detection which fuses depth embeddings converted
from depth map. We adopt the transformer-based
framework and transform the depth map into the
depth embeddings to encode depth information.
Parameters:
alpha=0.2
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) 93.85 % 85.69 % 75.81 %
Car (Orientation) 93.39 % 84.81 % 74.87 %
Car (3D Detection) 24.33 % 15.62 % 12.62 %
Car (Bird's Eye View) 33.32 % 21.14 % 18.37 %
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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