Method

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds [la] [Complexer-YOLO]
[Anonymous Submission]

Submitted on 16 Nov. 2018 13:55 by
[Anonymous Submission]

Running time:0.06 s
Environment:GPU @ 3.5 Ghz (C/C++)

Method Description:
Extension of ComplexYOLO, fusion with pixelwise
semantic segmentation
Parameters:
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) 88.11 % 79.31 % 79.11 %
Car (Orientation) 87.97 % 79.08 % 78.75 %
Car (3D Detection) 55.63 % 49.44 % 44.13 %
Car (Bird's Eye View) 74.23 % 66.07 % 65.70 %
Pedestrian (Detection) 42.63 % 36.10 % 35.57 %
Pedestrian (Orientation) 37.80 % 31.80 % 31.26 %
Pedestrian (3D Detection) 19.45 % 15.32 % 14.80 %
Pedestrian (Bird's Eye View) 22.00 % 20.88 % 20.81 %
Cyclist (Detection) 65.82 % 57.53 % 57.47 %
Cyclist (Orientation) 64.51 % 56.32 % 56.23 %
Cyclist (3D Detection) 28.36 % 23.48 % 22.85 %
Cyclist (Bird's Eye View) 36.12 % 30.16 % 26.01 %
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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