Method

PointPillars 3Class mmdetection3d [PointPillars_mmdet3d]
[Anonymous Submission]

Submitted on 12 May. 2025 19:17 by
[Anonymous Submission]

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
This is the mmdetection3d implementation of
PointPillars using SECOND backbone. Unlike the
original PointPillars, where one model was trained
on Cars and the other was trained on Ped and Cyc.,
mmdetection3d trains their model on all three
classes at once.
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) 95.21 % 91.96 % 87.03 %
Car (Orientation) 94.78 % 91.19 % 86.17 %
Car (3D Detection) 83.51 % 73.13 % 68.02 %
Car (Bird's Eye View) 91.25 % 85.96 % 80.82 %
Pedestrian (Detection) 56.07 % 46.06 % 43.60 %
Pedestrian (Orientation) 40.90 % 33.65 % 31.50 %
Pedestrian (3D Detection) 39.38 % 32.10 % 29.54 %
Pedestrian (Bird's Eye View) 45.74 % 38.07 % 35.76 %
Cyclist (Detection) 80.55 % 66.84 % 60.70 %
Cyclist (Orientation) 78.34 % 63.69 % 57.51 %
Cyclist (3D Detection) 70.51 % 54.47 % 48.34 %
Cyclist (Bird's Eye View) 75.76 % 61.70 % 54.77 %
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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