Method

Semantic Centroids Guided CNN for 3D object detection [la] [CentrNet-v1]
[Anonymous Submission]

Submitted on 16 Oct. 2019 09:32 by
[Anonymous Submission]

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

Method Description:
We propose a novel 3D object detection network,
called CentrNet, which focuses on the semantic
relation among point clouds to distinguish
neighboring objects. We calculate the semantic
centroid for each point cloud subset and extract
features, which guides the network to be aware of
the semantic information carried by the point cloud
data.
Parameters:
learning rate= 0.002
batch size =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) 94.22 % 91.21 % 88.36 %
Car (Orientation) 93.79 % 90.48 % 87.43 %
Car (3D Detection) 85.40 % 75.76 % 70.29 %
Car (Bird's Eye View) 90.72 % 87.19 % 83.34 %
Pedestrian (Detection) 66.27 % 56.57 % 54.19 %
Pedestrian (Orientation) 46.21 % 39.83 % 38.05 %
Pedestrian (3D Detection) 50.86 % 41.50 % 38.24 %
Pedestrian (Bird's Eye View) 57.58 % 48.78 % 45.94 %
Cyclist (Detection) 78.90 % 62.99 % 56.46 %
Cyclist (Orientation) 78.10 % 62.11 % 55.54 %
Cyclist (3D Detection) 72.03 % 54.64 % 48.03 %
Cyclist (Bird's Eye View) 75.80 % 58.05 % 51.17 %
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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