Method

3D-BCM: Balanced Cascade Multi-modal Network for 3D Object Detection [3D-BCM]
[Anonymous Submission]

Submitted on 9 May. 2023 10:08 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Using a cascade RoI head based on multi-head
attention to refin proposals and fuse multi-modal
features. By adjusting cascade structure, multi-
modal branches are balanced.
Parameters:
num of attention heads = 4
poing refinement stages = 2
image refinement stages = 4
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) 98.50 % 95.47 % 92.70 %
Car (Orientation) 98.47 % 95.27 % 92.39 %
Car (3D Detection) 90.36 % 81.91 % 77.01 %
Car (Bird's Eye View) 94.64 % 90.95 % 86.16 %
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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