Recognizing Objects in 3D Data with Distinctive Self-Similarity Features

被引：0

作者：

You, Suya ^{[1
]}

Huang, Jing ^{[2
]}

机构：

[1] US Army, Res Lab, Playa Vista, CA 90094 USA

[2] Univ Southern Calif, Dept Comp Sci, Los Angeles, CA 90089 USA

来源：

AUTOMATIC TARGET RECOGNITION XXVIII | 2018年 / 10648卷

关键词：

3D local feature descriptor; self-similarity; shape representation; point clouds; 3D data; UNIQUE SIGNATURES; SURFACE; RECOGNITION; IMAGES; HISTOGRAMS;

D O I：

10.1117/12.2305110

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Local features with invariant descriptions are important for many tasks in image processing and computer vision. This paper presents a new local feature descriptor for 3D object and scene representation. The new descriptor, named 3D-SSIM, explores the internal geometric property of layout similarity of 3D objects to produce efficient feature representation. The 3D-SSIM is highly distinctive, quick to compute, and shows superior advantages in terms of robustness to noises, invariance to viewpoints, and tolerance to geometric distortions. We extensively evaluated performance of the new descriptor with various datasets.

引用

页数：9

共 24 条

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