Method

ViKIENet-R [ViKIENet-R]


Submitted on 14 Nov. 2024 15:21 by
yu zhuochen (Nanyang Technological University)

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose ViKIENet (Virtual Key Instance Enhanced
Network), a highly efficient and effective multi-
modal feature fusion framework which fuses the
features of virtual key instances (VKIs) with
those of LiDAR points in multiple stages. We
observed that using only VKIs can enhance the
detection performance similar to using all virtual
points. ViKIENet has three main components:
Semantic Key Instance Selection (SKIS), Virtual
Instance Focused Fusion (VIFF) and Virtual-
Instance-to-Real Attention (VIRA). ViKIENet-R and
VIFF-R are extended versions of ViKIENet and VIFF
that include rotationally equivariant features.
Parameters:
TBD
Latex Bibtex:
@InProceedings{Yu_2025_CVPR,
author = {Yu, Zhuochen and Qiu, Bijie and
Khong, Andy W. H.},
title = {ViKIENet: Towards Efficient 3D
Object Detection with Virtual Key Instance
Enhanced Network},
booktitle = {CVPR},
month = {June},
year = {2025},
pages = {11844-11853}
}

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) 95.89 % 97.40 % 92.63 %
Car (Orientation) 95.78 % 97.08 % 92.11 %
Car (3D Detection) 91.20 % 86.04 % 81.18 %
Car (Bird's Eye View) 94.87 % 91.56 % 88.55 %
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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