Method

Focal Sparse Convolutional Networks for 3D Object Detection [Focals Conv]
https://github.com/dvlab-research/FocalsConv

Submitted on 18 Nov. 2021 04:30 by
Li Le heng (Dalian University of Technology)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a new sparse convolution design for 3D object detection
(feasible for both lidar-only and multi-modal settings). It named Focal
s Conv. Detailed method can be found in our CVPR 2022 Paper
(https://arxiv.org/abs/2204.12463).
Parameters:
n/a
Latex Bibtex:
@inproceedings{focalsconv-chen,
title={Focal Sparse Convolutional Networks for 3D Object
Detection},
author={Chen, Yukang and Li, Yanwei and Zhang, Xiangyu and Sun,
Jian and Jia, Jiaya},
booktitle={Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition},
year={2022}
}

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) 96.30 % 95.28 % 92.69 %
Car (Orientation) 96.29 % 95.23 % 92.60 %
Car (3D Detection) 90.55 % 82.28 % 77.59 %
Car (Bird's Eye View) 92.67 % 89.00 % 86.33 %
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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