Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes

By I. Sipiran, Benjamin Bustos. The Visual Computer, 2011

539 citations recorded by the source corpus.

With the increasing amount of 3D data and the ability of capture devices to produce low-cost multimedia data, the capability to select relevant information has become an interesting research field. In 3D objects, the aim is to detect a few salient structures which can be used, instead of the whole object, for applications like object registration, retrieval, and mesh simplification. In this paper, we present an interest points detector for 3D objects based on Harris operator, which has been used with good results in computer vision applications. We propose an adaptive technique to determine the neighborhood of a vertex, over which the Harris response on that vertex is calculated. Our method is robust to several transformations, which can be seen in the high repeatability values obtained using the SHREC feature detection and description benchmark. In addition, we show that Harris 3D outperforms the results obtained by recent effective techniques such as Heat Kernel Signatures.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/147ff0d409468f815c219d980c7cdc42d5cb63c8

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A Comprehensive Performance Evaluation of 3D Local Feature Descriptors (2015, methodology): “We use uniform sampling (Rusu and Cousins 2011), Harris3D (Sipiran and Bustos 2011), ISS (Zhong 2009), and ISS with boundary point removal (ISS-BR) to detect the keypoints in each scene and model.”
Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “Sipiran and Bustos (2011) extended the well-known Harris operator (1988) into 3-Ddatawith an adaptive-scale determination technique.”
Learning class‐specific descriptors for deformable shapes using localized spectral convolutional networks (2015, background): “Another class of approaches try to bring successful models like SIFT [Low04] or shape context, [BMP00] from images to surfaces [SB11,KBLB12].”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, methodology): “For example, the Harris 3D [40], 2.”