Title
Broad-to-Narrow Registration and Identification of 3D Objects in Partially Scanned and Cluttered Point Clouds
Abstract
The new generation 3D scanner devices have revolutionized the way information from 3D objects is acquired, making the process of scene capturing and digitization straightforward. However, the effectiveness and robustness of conventional algorithms for real scene analysis are usually deteriorated due to challenging conditions, such as noise, low resolution, and bad perceptual quality. In this work, we present a methodology for identifying and registering partially-scanned and noisy 3D objects, lying in arbitrary positions in a 3D scene, with corresponding high-quality models. The methodology is assessed on point cloud scenes with multiple objects with large missing parts. The proposed approach does not require connectivity information and is thus generic and computationally efficient, thereby facilitating computationally demanding applications, like augmented reality. The main contributions of this work are the introduction of a layered joint registration and indexing scheme of cluttered partial point clouds using a novel multi-scale saliency extraction technique to identify distinctive regions, and an enhanced similarity criterion for object-to-model matching. The processing time of the process is also accelerated through 3D scene segmentation. Comparisons of the proposed methodology with other state-of-the-art approaches highlight its superiority under challenging conditions.
Year
DOI
Venue
2022
10.1109/TMM.2021.3089838
IEEE TRANSACTIONS ON MULTIMEDIA
Keywords
DocType
Volume
Three-dimensional displays, Feature extraction, Solid modeling, Shape, Object recognition, Histograms, Data models, point cloud registration, partially-scanned point clouds, saliency, weighted ICP, cluttered scene
Journal
24
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Gerasimos Arvanitis196.21
Evangelia I. Zacharaki200.34
Libor Vása3908.33
K. Moustakas428558.02