3D deep shape descriptor

Fang, Yi; Xie, Jin; Dai, Gaole; Wang, Meng; Zhu, Fan; Xu, Tiantian; Wong, Edward K.

doi:10.1109/cvpr.2015.7298845

Cited by 164 publications

(66 citation statements)

References 38 publications

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“…We will present an approach herein where a handcrafted high-dimensional feature descriptor is used as an input to a NN. This approach is similar to the ones proposed by Fang et al (2015) and Khoury et al (2017).…”

Section: Local Feature Descriptorsmentioning

confidence: 80%

Learned Compact Local Feature Descriptor for TLS-Based Geodetic Monitoring of Natural Outdoor Scenes

Gojčič¹,

Zhou²

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The advantages of terrestrial laser scanning (TLS) for geodetic monitoring of man-made and natural objects are not yet fully exploited. Herein we address one of the open challenges by proposing feature-based methods for identification of corresponding points in point clouds of two or more epochs. We propose a learned compact feature descriptor tailored for point clouds of natural outdoor scenes obtained using TLS. We evaluate our method both on a benchmark data set and on a specially acquired outdoor dataset resembling a simplified monitoring scenario where we successfully estimate 3D displacement vectors of a rock that has been displaced between the scans. We show that the proposed descriptor has the capacity to generalize to unseen data and achieves state-of-the-art performance while being time efficient at the matching step due the low dimension.

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Section: Local Feature Descriptorsmentioning

confidence: 80%

Learned Compact Local Feature Descriptor for TLS-Based Geodetic Monitoring of Natural Outdoor Scenes

Gojčič¹,

Zhou²

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To reduce the computation cost, we may relax the correspondence to region-to-region correspondence or even make no requirement for surface correspondence. For example, we may apply a spectrum analysis approach (Fang et al, 2015) to get a consistent eigen-shape description of the GMMS on each region or on the entire cortical surface. Taking advantage of the nice theoretic properties of diffusion geometry, the multi-scale GMMS will still carry rich intrinsic geometry information and we may improve the computation efficiency without substantially losing classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

Wang

2017

NeuroImage

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In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer’s disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer’s Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.

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“…In addition, there have been recent spectral features such as HKSs and WKSs, which were used broadly for local feature extraction (e.g. [SBR16, MBBV15, BMM*15, BLH*14, WJZ15, FXD*15], etc. ).…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data‐Driven 3D Shape Descriptors

Rostami

Bashiri

Rostami

et al. 2018

Computer Graphics Forum

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Recent advances in scanning device technologies and improvements in techniques that generate and synthesize 3D shapes have made 3D models widespread in various fields including medical research, biology, engineering, etc. 3D shape descriptors play a fundamental role in many 3D shape analysis tasks such as point matching, establishing point‐to‐point correspondence, shape segmentation and labelling, and shape retrieval to name a few. Various methods have been proposed to calculate succinct and informative descriptors for 3D models. Emerging data‐driven techniques use machine learning algorithms to construct accurate and reliable shape descriptors. This survey provides a thorough review of the data‐driven 3D shape descriptors from the machine learning point of view and compares them in different criteria. Also, a comprehensive taxonomy of the existing descriptors is proposed based on the exploited machine learning algorithms. Advantages and disadvantages of each category have been discussed in detail. Besides, two alternative categorizations from the data type and the application perspectives are presented. Finally, some directions for possible future research are also suggested.

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3D deep shape descriptor

Cited by 164 publications

References 38 publications

Learned Compact Local Feature Descriptor for TLS-Based Geodetic Monitoring of Natural Outdoor Scenes

Learned Compact Local Feature Descriptor for TLS-Based Geodetic Monitoring of Natural Outdoor Scenes

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

A Survey on Data‐Driven 3D Shape Descriptors

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