RaNeRF: Neural 3-D Reconstruction of Space Targets From ISAR Image Sequences

逆合成孔径雷达 人工智能 计算机科学 计算机视觉 合成孔径雷达 雷达成像 迭代重建 模式识别(心理学) 雷达 电信
作者
Afei Liu,Shuanghui Zhang,Chi Zhang,Shuaifeng Zhi,Xiang Li
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-15 被引量:2
标识
DOI:10.1109/tgrs.2023.3298067
摘要

Compared to 2D inverse synthetic aperture radar (ISAR) images of a space target, its 3D model can provide adequate details and accurate measurement parameters. However, it is challenging to tackle the problem of feature extraction and correlation during 3D reconstruction of space targets purely based on radar image sequences, due to their lack of clear evidence in imaging similarity compared to optical images. To address this problem, this paper proposes radar neural radiance fields (i.e. RaNeRF), which is a novel 3D reconstruction method using only observed ISAR image sequences. Firstly, the 3D structure of a target is represented as a continuous 6D function of space positions and viewing directions using a fully-connected deep network. Secondly, the relationship between the 3D structure and 2D ISAR images of the target is constructed to enable differential rendering of ISAR images. Our overall pipeline can thus be trained using the discrepancy between the modulus of rendered and observed ISAR images in a purely self-supervised manner without 3D supervision. Finally, the 3D mesh model of the target can be retrieved from the learned density field via marching cube. As a result, the proposed RaNeRF can directly reconstruct the 3D structure of targets without explicit feature extraction and correlation of ISAR image sequences. Both quantitative and qualitative results verify the effectiveness of the proposed method. Compared to conventional baseline methods using point clouds, our reconstructed structure is more complete and accurate. In addition, the optimized model can synthesize ISAR images at novel observation direction, which can be used for downstream tasks including data augmentation and target recognition.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
矮小的天菱完成签到,获得积分10
1秒前
长安发布了新的文献求助10
1秒前
4秒前
ddddddd完成签到,获得积分20
5秒前
章半仙完成签到,获得积分10
6秒前
7秒前
9秒前
amberzyc应助小远采纳,获得10
10秒前
qiongqiong完成签到,获得积分10
11秒前
淡定的依瑶完成签到,获得积分10
12秒前
江璃发布了新的文献求助10
14秒前
15秒前
16秒前
美丽的安珊完成签到,获得积分10
17秒前
17秒前
19秒前
Gilana完成签到,获得积分10
19秒前
xyh发布了新的文献求助10
19秒前
江璃完成签到,获得积分10
20秒前
TT发布了新的文献求助10
20秒前
美梦成真完成签到,获得积分10
21秒前
Gakay完成签到,获得积分10
21秒前
量子星尘发布了新的文献求助10
22秒前
szj完成签到,获得积分0
23秒前
旦皋完成签到,获得积分10
23秒前
赘婿应助花壳在逃野猪采纳,获得10
24秒前
卷卷完成签到,获得积分10
26秒前
JSY完成签到 ,获得积分20
26秒前
xyh完成签到,获得积分10
27秒前
小曾应助Florencia采纳,获得10
28秒前
神外王001完成签到 ,获得积分10
28秒前
33秒前
你是谁完成签到,获得积分10
34秒前
majf完成签到,获得积分10
35秒前
linhanwenzhou完成签到,获得积分10
35秒前
JSY关注了科研通微信公众号
35秒前
853225598完成签到,获得积分10
35秒前
798完成签到,获得积分10
36秒前
善学以致用应助董怼怼采纳,获得10
36秒前
妍儿完成签到,获得积分20
37秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029