ES4D-Net: Foreground-Aided 3d Object Detection Based on Extremely Sparse 4D Radar Point Cloud

Zhu Wang; Zhiyang Lu; Wankang Zeng; Xi Xuan; Zhuo Geng; Ming Cheng

doi:10.1109/ICASSP55912.2026.11461960

ES4D-Net: Foreground-Aided 3d Object Detection Based on Extremely Sparse 4D Radar Point Cloud

Zhu Wang
, Zhiyang Lu
, Wankang Zeng
, Xi Xuan
, Zhuo Geng
, Ming Cheng^*

^*Corresponding author for this work

Department of Linguistics and Translation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

4D radar has attracted attention in autonomous driving perception due to its low cost and resistance to weather interference. However, the extreme sparsity and noise problems of its point cloud pose huge challenges to 3D object detection. To address this, we propose a 3D detection network, ES4D-Net, for extremely sparse 4D radar point clouds. We exploit k-nearest neighbor density information with a density-aware enhancement network to improve feature representation and innovatively introduce a foreground-aided mechanism using a pre-trained predictor to segment the foreground point cloud. Finally, we perform weighted fusion based on the similarity between the full-view and foreground point cloud. Experimental results show that ES4D-Net achieves state-of-the-art performance on the public dataset while maintaining superior detection speed. ©2026 IEEE

Original language	English
Title of host publication	ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
Pages	8522-8526
ISBN (Electronic)	979-8-3315-6701-9
DOIs	https://doi.org/10.1109/ICASSP55912.2026.11461960
Publication status	Published - 21 Apr 2026
Event	2026 IEEE International Conference on Acoustics, Speech, and Signal Processing - Barcelona, Spain Duration: 4 May 2026 → 8 May 2026 https://2026.ieeeicassp.org/

Conference

Conference	2026 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2026
Place	Spain
City	Barcelona
Period	4/05/26 → 8/05/26
Internet address	https://2026.ieeeicassp.org/

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s)

Funding

No funding was received for conducting this study. The authors have no relevant financial or nonfinancial interests to disclose.

Research Keywords

4D radar
3D object detection
sparse point clouds
autonomous driving
k-nearest neighbor algorithm

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10.1109/ICASSP55912.2026.11461960

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@inproceedings{bfdeeffd78934c75809ff7fa233bfd73,

title = "ES4D-Net: Foreground-Aided 3d Object Detection Based on Extremely Sparse 4D Radar Point Cloud",

abstract = "4D radar has attracted attention in autonomous driving perception due to its low cost and resistance to weather interference. However, the extreme sparsity and noise problems of its point cloud pose huge challenges to 3D object detection. To address this, we propose a 3D detection network, ES4D-Net, for extremely sparse 4D radar point clouds. We exploit k-nearest neighbor density information with a density-aware enhancement network to improve feature representation and innovatively introduce a foreground-aided mechanism using a pre-trained predictor to segment the foreground point cloud. Finally, we perform weighted fusion based on the similarity between the full-view and foreground point cloud. Experimental results show that ES4D-Net achieves state-of-the-art performance on the public dataset while maintaining superior detection speed. {\textcopyright}2026 IEEE",

keywords = "4D radar, 3D object detection, sparse point clouds, autonomous driving, k-nearest neighbor algorithm",

author = "Zhu Wang and Zhiyang Lu and Wankang Zeng and Xi Xuan and Zhuo Geng and Ming Cheng",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s); 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2026 ; Conference date: 04-05-2026 Through 08-05-2026",

year = "2026",

month = apr,

day = "21",

doi = "10.1109/ICASSP55912.2026.11461960",

language = "English",

pages = "8522--8526",

booktitle = "ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

address = "United States",

url = "https://2026.ieeeicassp.org/",

}

Wang, Z, Lu, Z, Zeng, W, Xuan, X, Geng, Z & Cheng, M 2026, ES4D-Net: Foreground-Aided 3d Object Detection Based on Extremely Sparse 4D Radar Point Cloud. in ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 8522-8526, 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, 4/05/26. https://doi.org/10.1109/ICASSP55912.2026.11461960

ES4D-Net: Foreground-Aided 3d Object Detection Based on Extremely Sparse 4D Radar Point Cloud. / Wang, Zhu; Lu, Zhiyang; Zeng, Wankang et al.
ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2026. p. 8522-8526.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - ES4D-Net

T2 - 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing

AU - Wang, Zhu

AU - Lu, Zhiyang

AU - Zeng, Wankang

AU - Xuan, Xi

AU - Geng, Zhuo

AU - Cheng, Ming

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s)

PY - 2026/4/21

Y1 - 2026/4/21

N2 - 4D radar has attracted attention in autonomous driving perception due to its low cost and resistance to weather interference. However, the extreme sparsity and noise problems of its point cloud pose huge challenges to 3D object detection. To address this, we propose a 3D detection network, ES4D-Net, for extremely sparse 4D radar point clouds. We exploit k-nearest neighbor density information with a density-aware enhancement network to improve feature representation and innovatively introduce a foreground-aided mechanism using a pre-trained predictor to segment the foreground point cloud. Finally, we perform weighted fusion based on the similarity between the full-view and foreground point cloud. Experimental results show that ES4D-Net achieves state-of-the-art performance on the public dataset while maintaining superior detection speed. ©2026 IEEE

AB - 4D radar has attracted attention in autonomous driving perception due to its low cost and resistance to weather interference. However, the extreme sparsity and noise problems of its point cloud pose huge challenges to 3D object detection. To address this, we propose a 3D detection network, ES4D-Net, for extremely sparse 4D radar point clouds. We exploit k-nearest neighbor density information with a density-aware enhancement network to improve feature representation and innovatively introduce a foreground-aided mechanism using a pre-trained predictor to segment the foreground point cloud. Finally, we perform weighted fusion based on the similarity between the full-view and foreground point cloud. Experimental results show that ES4D-Net achieves state-of-the-art performance on the public dataset while maintaining superior detection speed. ©2026 IEEE

KW - 4D radar

KW - 3D object detection

KW - sparse point clouds

KW - autonomous driving

KW - k-nearest neighbor algorithm

U2 - 10.1109/ICASSP55912.2026.11461960

DO - 10.1109/ICASSP55912.2026.11461960

M3 - RGC 32 - Refereed conference paper (with host publication)

SP - 8522

EP - 8526

BT - ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

Y2 - 4 May 2026 through 8 May 2026

ER -

ES4D-Net: Foreground-Aided 3d Object Detection Based on Extremely Sparse 4D Radar Point Cloud

Abstract

Conference

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Research Keywords

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