Method

Representation Learning with Multi-scheme Voxelization for 3D Object Detection [MVOD]


Submitted on 17 Jun. 2021 02:35 by
yao gang (Tsinghua University)

Running time:0.16 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
ur main contribution is multi-scheme voxelization
strategy and implicit point completion based on
generative adversarial networks.
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) 96.17 % 93.16 % 92.56 %
Car (Orientation) 96.15 % 93.07 % 92.39 %
Car (3D Detection) 88.53 % 80.01 % 77.24 %
Car (Bird's Eye View) 92.50 % 88.85 % 86.19 %
Pedestrian (Detection) 66.72 % 57.33 % 54.31 %
Pedestrian (Orientation) 63.82 % 53.49 % 50.20 %
Pedestrian (3D Detection) 46.22 % 39.82 % 37.56 %
Pedestrian (Bird's Eye View) 50.38 % 44.32 % 42.37 %
Cyclist (Detection) 85.92 % 77.92 % 71.24 %
Cyclist (Orientation) 85.72 % 77.59 % 70.91 %
Cyclist (3D Detection) 79.52 % 64.95 % 57.53 %
Cyclist (Bird's Eye View) 82.85 % 69.37 % 61.93 %
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.
This figure as: png eps pdf txt gnuplot




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