Method

: Geometry-Aware Voxel 3D Object Detector from Point Cloud for Autonomous Vehicles [GeVo]


Submitted on 30 Aug. 2024 08:23 by
He Zhenyu (Hunan University)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
we design a Geometry-Aware Voxel Feature Embedding
module(GeVFE) to embed the low-level feature
information from
the voxel encoding network into the high-
resolution semantic
feature maps, and uses appropriate kernel
functions to enhance
and preserve the geometric information during the
embedding
process
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) 95.91 % 94.83 % 92.48 %
Car (Orientation) 95.88 % 94.75 % 92.36 %
Car (3D Detection) 90.60 % 82.13 % 79.51 %
Car (Bird's Eye View) 92.58 % 90.74 % 88.36 %
Pedestrian (Detection) 69.06 % 60.99 % 58.89 %
Pedestrian (Orientation) 63.53 % 55.17 % 52.79 %
Pedestrian (3D Detection) 52.89 % 44.63 % 42.36 %
Pedestrian (Bird's Eye View) 58.94 % 51.34 % 49.21 %
Cyclist (Detection) 84.14 % 76.02 % 70.24 %
Cyclist (Orientation) 83.97 % 75.67 % 69.88 %
Cyclist (3D Detection) 78.88 % 64.36 % 58.60 %
Cyclist (Bird's Eye View) 80.78 % 68.06 % 62.15 %
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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