Method

Bi-Attention Fusion PartA^2-S-4h [BAPartA2S-4h]


Submitted on 9 Mar. 2024 08:53 by
Touch Fish (USTC)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
We adopted a cascaded bidirectional attention
fusion module to introduce image information into
Part A ^ 2, achieving adaptive Lidar Camera fusion
at the depth feature level without the need for a
projection matrix to align the information of the
two modalities. All experiments were based on the
latest version 1.4.0 of MMDetection3d. Compared to
the PartA ^ 2 baseline (mm3d_PartA2) reproduced
using MMDetection3d V1.4.0, our BAPartA2 was
overall better than the baseline, especially in
Cyclist and Pedestrian
Parameters:
4 attention heads
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) 95.82 % 92.53 % 89.80 %
Car (Orientation) 95.63 % 92.04 % 89.14 %
Car (3D Detection) 86.97 % 76.31 % 73.03 %
Car (Bird's Eye View) 91.96 % 87.89 % 83.31 %
Pedestrian (Detection) 64.22 % 54.34 % 51.53 %
Pedestrian (Orientation) 60.15 % 50.21 % 47.41 %
Pedestrian (3D Detection) 47.67 % 39.21 % 36.53 %
Pedestrian (Bird's Eye View) 53.05 % 44.18 % 41.54 %
Cyclist (Detection) 89.14 % 77.04 % 71.00 %
Cyclist (Orientation) 88.80 % 76.18 % 70.06 %
Cyclist (3D Detection) 79.31 % 63.07 % 57.44 %
Cyclist (Bird's Eye View) 82.22 % 67.05 % 61.08 %
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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