Method

CAFI-Pillars [CAFI-Pillars]
[Anonymous Submission]

Submitted on 18 Apr. 2023 03:37 by
[Anonymous Submission]

Running time:30ms
Environment:NVIDIA Tesla P40 GPU

Method Description:
Pillar-based feature learning patterns have
demonstrated high efficiency for 3D object
detection, fostering the research of environmental
perception for autonomous vehicles. However,
aggressive downsampling during pillarization leads
to a problem of lacking explicit geometry clues in
pillar vectors. To address this limitation, we
consider infusing more spatial information as
prior knowledge for pillar-based feature
abstraction.
Parameters:
TBD
Latex Bibtex:
TBD

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) 96.47 % 95.56 % 90.75 %
Car (Orientation) 96.46 % 95.43 % 90.56 %
Car (3D Detection) 87.66 % 81.53 % 77.00 %
Car (Bird's Eye View) 92.32 % 88.96 % 86.24 %
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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