A New Method of Gold Foil Damage Detection in Stone Carving Relics Based on Multi-Temporal 3D LiDAR Point Clouds

Hou, Miaole; Li, Shukun; Jiang, Lili; Yu-hua, WU; Hu, Yungang; Su, Yang; Zhang, Xuedong

doi:10.3390/ijgi5050060

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…As there is a lack of large-scale data sets of partial views of real objects, Par3DNet [38] is proposed to fill the gap between synthetic and real data, which can take a partial 3D view of the object as an input and is able to accurately classify it. Hou et al [39] propose a novel method for detecting gold foil damage on stone carving relics by making use of multi-temporal 3D LiDAR point cloud.…”

Section: Deep Learning On Point Cloudsmentioning

confidence: 99%

AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

et al. 2021

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As an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. However, most of the existing methods are based on traditional technology and have low accuracy in classification. A practical and effective classification method for fragments is an urgent need. In this case, an attention-based multi-scale neural network named AMS-Net is proposed to extract significant geometric and semantic features. AMS-Net is a hierarchical structure consisting of a multi-scale set abstraction block (MS-BLOCK) and a fully connected (FC) layer. MS-BLOCK consists of a local-global layer (LGLayer) and an improved multi-layer perceptron (IMLP). With a multi-scale strategy, LGLayer can parallel extract the local and global features from different scales. IMLP can concatenate the high-level and low-level features for classification tasks. Extensive experiments on the public data set (ModelNet40/10) and the real-world Terracotta Warrior fragments data set are conducted. The accuracy results with normal can achieve 93.52% and 96.22%, respectively. For real-world data sets, the accuracy is best among the existing methods. The robustness and effectiveness of the performance on the task of 3D point cloud classification are also investigated. It proves that the proposed end-to-end learning network is more effective and suitable for the classification of the Terracotta Warrior fragments.

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Section: Deep Learning On Point Cloudsmentioning

confidence: 99%

AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

et al. 2021

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“…The first type applies to symmetric objects that can be divided into two or more identical pieces, so undamaged pieces can be used as the basis for reconstructing damaged pieces. This type of method has been studied on small portable symmetric antiquities such as potteries and vases [4][5][6] and sizeable immovable grotto Buddhist statues such as the Thousand-hand Bodhisattva in Dazu, China [7][8][9][10]. The second type of method focuses on a cluster of heritage objects, such as the terracotta warriors in China [11] and the human face statues of the Bayon temple in Cambodia [12,13], which may be similar or have similar or repetitive parts that can be used as references for reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Similarity Index Based Approach for Identifying Similar Grotto Statues to Support Virtual Restoration

et al. 2021

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Grottoes, with caves and statues, are an important part of immovable heritage. Statues in a particular grotto setting are often similar in geometric form and artistic style, and identifying the similarity between these statues can help provide important references for value recognition, condition assessment, repair, and the virtual restoration of statues. Traditionally, such reference information mainly depended on expert empirical judgment, which is highly subjective, lacks quantitative analysis, and cannot provide effective scientific support for the virtual restoration of grotto statues. This paper presents a similarity index based approach for identifying similarities between grotto statues by studying 11 small Buddhist statues carved on the 18th cave in the Yungang Grottoes, located in Datong, China. The similarity index is determined according to the hash values calculated based on the pHash method using the orthophoto images of Buddhist statues to identify similar statues. Similar feature points between the identified statues are then matched using the Scale Invariant Feature Transform (SIFT) operator to support the repair and reconstruction of damaged statues. The experimental results show that the variation of similarity index values confirms the visual inspection of the statues’ appearance in the orthophotos. The additional analysis of three-dimensional (3D) point clouds also confirms that the similarity index based approach is accurate in the initial screening of similar grotto statues.

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“…However, because the stone carvings are immovable cultural relics, they are more likely to be eroded by diseases compared with other cultural relics. In addition, the varieties and quantity of stone carving diseases are complex, which brings great difficulty to information collection and analysis (Hou et al, 2016a;Wang et al, 2019;Wang et al, 2018;Wenzei, 2016).…”

Section: Introductionmentioning

confidence: 99%

Disease Investigation of Stone Carving by Spatial Analysis: A Case Study on the Stone Carving of Dazu Thousand-Hand Bodhisattva

Yang

Hou

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Cultural relics are often threatened by nature and human, especially stone carving relics which have immovable characteristics. Compared with other cultural relics, the diseases of stone carving relics are more complex, and they can affect carvings’ cultural and artistic value to a great extent. This article selects Dazu thousand-hand bodhisattva as a case, not only because it has a strong representation in Chinese stone carving art, but also considering that there are many complex diseases in the whole range after a long history, therefore the Dazu thousand-hand bodhisattva has high research value, and scientific investigation methods are essential for the protection and research of cultural relics. The main purpose of this paper is to investigate the arm diseases of Dazu thousand-hand bodhisattva by using the two methods of GIS spatial analysis: kernel density analysis and trend analysis. The past investigation methods are difficult to achieve the expected results because of their strong subjectivity and narrow range, and the use of spatial analysis to investigate the diseases of stone carvings can study the spatial characteristics and coupling relationship of stone carvings from the macro level.

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A New Method of Gold Foil Damage Detection in Stone Carving Relics Based on Multi-Temporal 3D LiDAR Point Clouds

Cited by 16 publications

References 20 publications

AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

AMS-Net: An Attention-Based Multi-Scale Network for Classification of 3D Terracotta Warrior Fragments

Similarity Index Based Approach for Identifying Similar Grotto Statues to Support Virtual Restoration

Disease Investigation of Stone Carving by Spatial Analysis: A Case Study on the Stone Carving of Dazu Thousand-Hand Bodhisattva

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