Radar-Based Shape and Reflectivity Reconstruction Using Active Surfaces and the Level Set Method

被引:3
作者
Bignardi, Samuel [1 ]
Sandhu, Romeil [2 ]
Yezzi, Anthony
机构
[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
[2] SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA
基金
美国国家科学基金会;
关键词
Image reconstruction; Radar; Surface reconstruction; Radar imaging; Shape; Three-dimensional displays; Radar antennas; Active surfaces; high frequency; Index Terms; inversion; level set method; noncoherent; radar; reflectivity estimation; shape reconstruction; SAR IMAGE SEGMENTATION; FRAMEWORK; CONTOURS;
D O I
10.1109/TPAMI.2022.3178969
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We investigate a multiview shape reconstruction problem based on an active surface model whose geometric evolution is driven by radar measurements acquired at sparse locations. Building on our previous work in the context of variational methods for the reconstruction of a scene conceptualized as the graph of a function, we generalize this inversion approach for a general geometry, now described by an active surface, strongly motivated by prior variational computer vision approaches to multiview stereo reconstruction from camera images. While conceptually similar, use of radar echoes within a variational scheme to drive the active surface evolution requires significant changes in regularization strategies compared to prior image based methodologies for the active surface evolution to work effectively. We describe all of these aspects and how we addressed them. While our long term objective is to develop a framework capable of fusing radar as well as other image based information, in which the active surface becomes an explicit shared reference for data fusion. In this paper, we explore the reconstruction using radar as a single modality, demonstrating that the presented approach can provide reconstructions of quality comparable to those from image based methods showing great potential for further development toward data fusion.
引用
收藏
页码:3617 / 3631
页数:15
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