Method

Online Semantic Embedding[st] [OSE]
[Anonymous Submission]

Submitted on 3 Jul. 2020 09:04 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
Online Semantic Embedding (OSE) bridges image semantics with the pseudo-LiDAR detection in an end-to-end manner, enabling the semantic features to be better aligned with the final detection objectives.
Parameters:
TBA
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) 95.64 % 86.21 % 76.83 %
Car (Orientation) 95.15 % 84.75 % 75.34 %
Car (3D Detection) 64.78 % 43.27 % 37.13 %
Car (Bird's Eye View) 79.75 % 58.04 % 49.78 %
Pedestrian (Detection) 50.26 % 38.62 % 34.87 %
Pedestrian (Orientation) 40.27 % 30.02 % 26.86 %
Pedestrian (3D Detection) 28.68 % 20.65 % 17.65 %
Pedestrian (Bird's Eye View) 33.00 % 23.62 % 20.35 %
Cyclist (Detection) 45.05 % 28.78 % 25.66 %
Cyclist (Orientation) 38.00 % 23.93 % 21.58 %
Cyclist (3D Detection) 28.50 % 17.28 % 15.56 %
Cyclist (Bird's Eye View) 32.06 % 19.41 % 17.42 %
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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