Method

MLFusion-VS: Multi-level Fusion Cross-modal 3D Object Detection with Dense Voxel Segmentation [MLFusion-VS]


Submitted on 2 May. 2024 12:51 by
Zheng Li (Northeastern University)

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

Method Description:
Multi-level fusion cross-modal 3D object detection.
Parameters:
TBD
Latex Bibtex:
TBD

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) 98.26 % 94.79 % 92.43 %
Car (Orientation) 98.25 % 94.68 % 92.25 %
Car (3D Detection) 91.12 % 83.71 % 79.74 %
Car (Bird's Eye View) 95.10 % 90.78 % 88.41 %
Pedestrian (Detection) 66.13 % 57.22 % 55.00 %
Pedestrian (Orientation) 62.55 % 53.46 % 50.98 %
Pedestrian (3D Detection) 46.60 % 40.25 % 38.30 %
Pedestrian (Bird's Eye View) 50.74 % 45.27 % 43.28 %
Cyclist (Detection) 83.72 % 75.35 % 69.56 %
Cyclist (Orientation) 83.55 % 74.99 % 69.20 %
Cyclist (3D Detection) 78.76 % 64.48 % 58.61 %
Cyclist (Bird's Eye View) 80.99 % 68.43 % 62.46 %
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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