Method

A pillar-based cascaded 3D object detection model with an enhanced 2D backbone [PCNet3D++]


Submitted on 21 Oct. 2025 15:03 by
THURIMERLA PRASANTH (Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram)

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
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Parameters:
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Latex Bibtex:
@article{prasanth2025pcnet3d++,
title={PCNet3D++: A pillar-based cascaded 3D
object detection model with an enhanced 2D
backbone},
author={Prasanth, Thurimerla and Padhy, Ram
Prasad and Sivaselvan, B},
journal={Image and Vision Computing},
pages={105854},
year={2025},
publisher={Elsevier}
}

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) 94.96 % 91.90 % 88.61 %
Car (Orientation) 94.93 % 91.72 % 88.32 %
Car (3D Detection) 84.00 % 74.19 % 69.65 %
Car (Bird's Eye View) 90.13 % 87.17 % 83.10 %
Pedestrian (Detection) 62.37 % 51.69 % 49.14 %
Pedestrian (Orientation) 55.89 % 45.03 % 42.29 %
Pedestrian (3D Detection) 45.22 % 37.47 % 34.95 %
Pedestrian (Bird's Eye View) 50.62 % 42.81 % 40.36 %
Cyclist (Detection) 82.71 % 70.28 % 64.10 %
Cyclist (Orientation) 82.16 % 69.59 % 63.42 %
Cyclist (3D Detection) 76.16 % 58.67 % 52.02 %
Cyclist (Bird's Eye View) 80.19 % 64.20 % 57.09 %
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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