ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

SURFACE OR SKELETON? AUTOMATIC HIERARCHICAL CLUSTERING OF 3D
POINT CLOUDS OF BRONZE FROG DRUMS FOR HERITAGE DIGITAL TWINS
Fan Xue1,*, Wenjin Zhang2; Guangji Xu3, Qianyun Zhou1, Yijie Wu1
1 The University of Hong Kong, Hong Kong S.A.R. (China) – xuef@hku.hk, qianyunz@hku.hk, yijiewu@connect.hku.hk
2 Guangzhou Okay Information Technology Ltd., Guangzhou,
3 Anthropology Museum of Guangxi,

China – zhangwenjin@okaygis.com
Nanning, China – 9115857@qq.com

KEY WORDS: Digital heritage, bronze frog drum, 3D point cloud, 3D shape skeleton, hierarchical clustering, heritage digital twin.

ABSTRACT:
In the era of digital twins, high-definition 3D point clouds of cultural relics, such as the bronze drums of ancient Southeast Asia and
China, are increasingly available as digital heritage. This study applies an automatic hierarchical clustering method to compare and
cluster 14 unstructured 3D models of frogs on drums based on the dissimilarity metric of the minimum error from 2,000 iterations of
global registration. Furthermore, this study compares two forms of 3D presentation: surface points and 3D shape skeletons. The
experimental results on 14 high-definition frogs showed that four groups – three-legged with baby, four-legged with baby, threelegged without baby, and four-legged without baby – were consistently (TPR = 0.857) detected, regardless of the 3D presentation
using point clouds or shape skeletons. Both basic surface points and advanced shape skeleton effectively clustered 3D heritage
details for heritage digital twins and advanced heritage documentation. The findings also imply that geospatial analytics using either
surface 3D point clouds or skeleton can shed light on unsupervised learning and quantitative understanding of unstructured point
clouds of numerous cultural heritages.

1. INTRODUCTION
With the advancing remote sensing and geospatial technologies,
cultural heritage has increasingly embraced digital modeling
and digital twins. Digital heritages can preserve geometries,
identify art forgeries, and strengthen the interaction of digital
multimedia with a broad audience (Gomes et al., 2014). 3D
color meshes and point clouds are popular formats of digital
heritage. However, both meshes and point clouds are often
unstructured data that exist in native or raw forms of points and
tiny triangles (Zhang et al., 2021). They can support
visualization and visual analysis but lack semantic information
and knowledge about cultural heritages (Yang et al., 2021).
Heritage Digital Twins (HDT) and advanced heritage
documentation demand automatic processing methods and
quantitative understandings of unstructured digital heritage data
(Niccolucci et al., 2022). For example, an HDT can have a rich
list of semantic properties and recommended hyperlinks to
similar HDTs based on the user’s preferences. On the one hand,
the mm-accurate 3D sensor data enables new quantitative
evidence and analytics for supporting and supplementing
heritage science and conservation studies. On the other hand,
the high-level complexity of 3D data, the diversity of assets, and
the sheer quantities of parts, patterns, and details make it
difficult to analyze, classify, and interpret digital heritages in
traditional ways (Grilli and Remondino, 2019; Chen and Xue,
2023). Thus, geospatial technologies, such as semantic
segmentation of 3D data, have been studied recently (Yang et
al., 2023).
Bronze drums are body-decorated percussion instruments with a
single head and a curving waist. They originated in central and
western Yunnan in the eighth century BC and spread across

Fig. 1. Eight types of ancient bronze drums in China. A.
Wanjiaba type; B. Shizhaishan type; C. Lengshuichong type;
D. Beiliu type; E. Lingshan type; F. Zunyi type; G. Majiang
type; H. Ximeng type. (Source: Lu et al., 2020, CC-BY 4.0)
South China and Southeast Asia as spiritual, sacrifice, and
musical instruments (Lu et al., 2020). Today, China has
preserved over 1,500 ancient bronze drums of various types,
and hundreds more are found in Burma, Laos, Thailand, and
Vietnam (Cooler, 1995). A bronze drum is divided into three
parts: the drum face, the drum body, and the drum foot, which

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

293

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

correlate to the sky, earth, and underground of the universe. The
ornamental patterns that surround the drum are classified into
five major content categories: geometric, animal, plant,
religious, and narrative patterns. Typically, three-dimensional
statues, such as frogs, are frequently used to embellish the
margins of the drum surface (Zhang and Kamal, 2022).
Bronze drums in China, for example, can be classified into eight
types according to their scale and excavation locations, as
shown in Figure 1. The frog drums, as shown in types C, D, and
E in Fig. 1, are larger in size and all have frogs as decorations.
Usually, a frog drum has a face diameter of 70 to 100 cm,
whereas the D type has the widest face diameter of 165 cm
(Tang, 2007). On the top, frog decorations indicate rains and
prosperous offspring, while the solar pattern in the middle of a
drum top standards for aspiration for sunlight.
The decorations on the drums are believed to be associated with
totem worship traditions of Karen, Zhuang, Wa, and other
ethnicities that evolved over history. For example, Zhou
(1178/2018) reported, “bronze drums often unearthed in
Guangxi by the tillers … with a perfect circle with bent body …
five sitting frogs, each with a baby on its back.” Zhu
(1948/1961) noted that “surrounding frogs indicate [the chief’s]
title; the more frogs, the more honorable title.” In short, the frog
drums’ types, designs, patterns, and decorative details such as
frogs and babies on back have long evolved. Thus, it is of
interest in cultural, musical, and historical studies to analyze the
evolutionary designs and production of frog drums using
interdisciplinary qualitative and quantitative research methods,
such as geospatial technologies and machine learning.
Geospatial technologies were applied to the quantitative study
topics about bronze drums, too (Lu et al., 2020). The
digitalization of ancient bronze drums entails close-up
photogrammetry, high-definition digital image capture, laser
scanning technology (Gomes et al., 2014), recording and
preservation of text, audio, video, and 3D analysis and
modeling (Lan, 2015). Multi-baseline stereo matching, multibeam forward intersection, and block adjustment have been
employed to study feature matching and multi-baseline stereo
positioning, thereby enhancing the reliability and accuracy of
point cloud data on the surface of the bronze drums (Zhang,
2017). However, the manual 3D analysis, modeling, and
enrichment of digital heritage require significant time, space,
and human resources. Therefore, there is a strong need for
automatic processing of the 3D details of digital bronze drums.
Numerous 3D processing techniques utilizing supervised
learning encounter challenges when dealing with heterogeneous
forms, non-parametric shapes, and sophisticated decorations
against maximum fidelity (Gomes et al., 2014). Additionally,
the processing of unstructured points to elaborate bronze drum
ornamentation can be massive, and volumetric and semantic
data about digital bronze drums may be unavailable. The school
of supervised machine learning is thus heavily limited by the
difficulties above. Unsupervised learning techniques were thus
studied for processing similarity in 3D point clouds.
3D similarity between point clouds, in general, can be measured
as the root-mean-squared error (RMSE), mismatching ratio, or
heterogeneity functions defined on basic 3D features, such as
geometry (e.g., points, edges, and primitives), color, normals,
and curvatures (Alexiou and Ebrahimi, 2020). Advanced
similarity measures, such as cross sections for urban objects
(Xue et al., 2020), shape skeletons for coral and tree details

Fig. 2. Research methods of this paper.
(Huang et al., 2013), and graph models of persons (Yang et al.,
2022), have demonstrated proficiency in comprehending the
unstructured and non-parametric 3D point cloud data. Example
applications of 3D similarity in heritage documentation include
clustering of ancient Chinese bridge detailing for HDTs (Pan et
al., 2019), 3D motifs extraction (Yunus et al., 2021), contour
surface alignment for digital restoration of decorated fragments
(Hernandez et al., 2019), and hierarchical clustering of 3D
points in the HSV space for automatic alternation detection
(Musicco et al., 2021). In summary, many basic 3D similarity
measures were successfully adopted for a variety of heritage
documentation purposes in the literature; Yet the pros and cons
of advanced measures such as shape skeletons and graph
models are not well studied.
This paper aims to apply an unsupervised hierarchical clustering
method to understand the 3D details of bronze frog drums, and
compare the basic surface point-based dissimilarity measure and
a shape skeleton-based one. First, we adopt the RMSE
dissimilarity definition in (Xue et al., 2020), then apply an
optimization algorithm to compute the dissimilarity. Finally,
hierarchical clustering groups similar frogs together. With
clustering results from 3D point clouds and shape skeletons of
the same frogs, the representativeness of the two 3D forms can
be compared. Encouraging results were observed. The
contribution of this paper, thus, lies in (i) proving the concept of
automatic hierarchical clustering of complex heritage objects
such as bronze frogs and (ii) pinpointing evidence that surface
point clouds and shape skeletons are both appropriate for
understanding the (dis)similarities and clustering HDTs.

2. RESEARCH METHODS
Figure 2 shows the technical processes employed in this study,
which involved two input datasets of 3D point clouds and shape
skeletons of frog drums. The focus of this study is on decorative
frogs. Initially, we collected 3D point clouds of frog drums and
computed the shape skeleton for each point cloud. Following
this, we calculated pairwise dissimilarities in each dataset using
the least global registration error matrix. The dissimilarity
matrix triggered an automatic hierarchical clustering process for
grouping geometrically similar objects. No training datasets or
human annotations are required in this unsupervised learning
process. Finally, we quantitatively compared the grouping

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

294

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

cylinder models, to make them consistent in format and size
with the point clouds.
2.2 Dissimilarity definition
This paper adopts the most well-known basic metric for
measuring dissimilarity between a pair of surface point clouds,
i.e., the minimum RMSE (Xue et al., 2020):
dissimilarity(Ci, Cj) =
minr,t∈ℝ³ RMSE(Ci, trans(rot(Cj, r),t)),

(1)

where the RMSE function returns the error between the surface
point cloud pair (Ci, Cj); r = [rx, ry, rz] is the set of 3D rotation
parameters defined on ℝ³, and t = [tx, ty, tz] is the set of 3D
translation parameters. That is, the dissimilarity in the least
global registration error of the two point clouds. The RMSE
exploits the octree data structure to compute the nearest points.
The RMSE computation also reuses the maximum-depth
sampling of Cj in Xue et al. (2019) for an efficient computing,
which is set to depth = 6 in this paper.

Fig. 3. Collection of 14 frogs on three drums in Anthropology
Museum of Guangxi (color in the right figures indicates
height). (a) Close-range LiDAR and photogrammetry; (b)
realistic 3D drum models; (c) 14 frog models segmented.
results, assessed the meaningfulness of the results, and
identified any anomalies from the two 3D forms of frogs.
2.1 Data collection and preprocessing
Figures 3a and 3c showcase the three bronze frog drums
selected from the Anthropology Museum of Guangxi, China,
identified by registration IDs 60, 104, and 139. As shown in
Figure 3a, a close-range LiDAR device MarvelScan was
adopted for scanning mm-accurate 3D point clouds, and a
digital camera Sony Alpha A7 for 3D true-color textures. The
team fused the two data sources as color mesh models, where
this paper focuses on the decorative frogs’ details.
A total of 14 frogs were segmented after removing the drum
top’s plane. The frogs were named clockwise, as depicted in
Figure 3c. Subsequently, all frog mesh models were scaled to a
uniform height of 1.0 and uniformly sampled into 100,000
colorless points to control the triangle density and colors. This
process generated a collection of 14 point clouds, each
containing approximately 100,000 points and occupying an
estimated 2.6MB (.PLY format) of disk space. It is important to
note that the heading direction of each frog was preserved from
the drum model.
The final step of preprocessing is the preparation of 3D shape
skeletons. In this study, we apply the axial skeleton in the
computational geometry algorithms library (CGAL, ver. 5.3) to
triangulated surface meshes of frogs (Gao et al., 2015). To
control the variables of skeleton point density and normals, we
created very-thin cylinders connecting the closest skeleton
points for each frog, using the octree data structure in the
open3d package (ver. 0.15, in Python 3.8). Then, 100,000
colorless points were uniformly sampled from the curvature-like

For a pair of continuous 3D shape skeletons Si and Sj, we apply
a dense sampling process to convert the skeletons into
associated point clouds CSi and CSj. The dissimilarity can then
be computed from the two dense point clouds using the same
Eq. (1).
To solve the minimization problem in Eq. (1) for measuring
dissimilarity, an iterated algorithm DIviding RECTangle
(DIRECT) is applied. The DIRECT is proven efficient and
effective in the global registration of noisy point clouds (Wu et
al., 2021; Xue et al., 2020). In contrast, some conventional
point clouds registration methods such as iterative closest point
(ICP) are not good at global registration of complex point
clouds (Wu et al., 2021). The GN_DIRECT module in the nlopt
package (https://github.com/stevengj/nlopt, ver. 2.7) provides a
fast implantation of the DIRECT algorithm. The maximum
iteration, i.e., the primary parameter, of the DIRECT algorithm
was set to 2,000.
The pairwise problem solving of the dissimilarity values
between N objects yields a N×N dissimilarity matrix via Eq.
(1). The diagonal elements of the dissimilarity matrix are all 0,
and the data is spread symmetrically diagonally.

2.3 Automatic
threshold

hierarchical

clustering

with

adaptive

A hierarchical structure can be established from the
dissimilarity matrix generated in Section 2.2 by gradually
pairing up the most similar items. The hierarchy clustering
(hclustering) module in the scipy package (ver. 1.8) provides
cluster visualization with dendrograms. A tree form represents
the cluster hierarchy. The roots reflect unique groups of values,
and the leaves are composed of single sample values.
The threshold to cut the hierarchical tree into groups is adaptive
in this paper. It is defined as average dissimilarity of the
dendrogram’s lowest branch and the root (highest branch). For
example, if the root sits at dissimilarity = 0.3 and the lowest
branching is dissimilarity = 0.2, the threshold of grouping will
be 0.25. One can lift the threshold to a greater value to have
fewer but more general groups; similarly, one can tighten it to
for more groups.

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

295

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

2.4 Comparative analysis
In this step, we employ a multi-class confusion matrix, also
known as an error matrix, to compare the two sets of grouping
results. A confusion matrix is a statistical tool that can compare
classification results against the ground truth. The confusion
matrix is a popular analytical tool in the literature of data
sciences. Table 1 shows an example confusion matrix with nine
cells for three classes. The three diagonal cells represent the true
positives (TPs) of the three classes. The non-diagonal cells
represent incorrect predictions, such as one X being predicted
as Z and one Y being predicted as Z in the upper triangle in
Table 1. The true positive rate (TPR) in Table 1 is thus
calculated as (3+3+2)/10 = 0.8. In this study, we use results of
basic 3D point-based clustering to group the rows and those of
shape skeletons to group the columns. To enhance visual
clarity, the preferred diagonal cells are highlighted in bold fonts
and cool color backgrounds, and erroneous cells in warm color
backgrounds, while the zero values have been omitted.

Truth

X
Y
Z

Predicted as
X
Y
3
3

Z
1
1
2

Table 1. Example confusion matrix for classifying 10 objects
into 3 classes.

Fig. 4. Dissimilarity matrices between the 14 frogs (cold color
indicates similar). (a) Surface point clouds; (b) shape skeleton

In summary, the dissimilarity definition in Sect. 2.2 and the
automatic, adaptive hierarchical clustering in Sect. 2.3
collectively respond to the research objective of complex 3D
heritage objects understanding, using the bronze frogs as a case
study. The group level comparison in Sect. 2.4 further examines
whether the clustering results are consistent from basic surface
points and advanced shape skeletons.

from a worn frog ornament. The group in light yellow consists
of three objects, i.e., B2, B4, and B6, which are the three-legged
frogs on drum B with even serial numbers. The other group of
“with baby” in green included A1, A2, and A3, which are the
big four-legged frogs on drum A. The two groups in red and
blue did not carry babies; they were the odd sequence of threelegged frogs on drum B and four-legged frogs on drum C,
respectively. The unusual individual was A4, which was a worn
frog ornament with no more details on the head and back.
Overall, the clustering results at adaptive threshold = 0.11, as
shown in Fig. 5a, were reasonable and successful.

3. RESULTS
3.1 Hierarchical clustering
Fig. 4 shows the two dissimilarity matrices of point clouds and
shape skeletons, respectively. In the visualized dissimilarity
matrix in Fig. 4a, the highest dissimilar between frogs’ point
clouds was 0.2407 between A4 and B6. The objects closet to
A4 were C1 and C2, with a dissimilarity value at 0.14 (rounding
of 0.1393). Meanwhile, the lowest dissimilar was 0.08
(rounding of 0.0790) between B1 and B5, and between B4 and
B6. Thus, the grouping threshold for 3D point clouds was
(0.14+0.08) / 2 = 0.11. Similarly, the lowest dissimilar was 0.11
between B4 and B6 in Fig. 4b, while A4’s lowest dissimilarity
was 0.24. So, the threshold was 0.175 for the shape geometry’s
results. Overall, the color distribution patterns in the subfigures
are alike in Fig. 4, where A1 was an exception.
Figure 5 manifests the results of four groups from the thresholds
and distinguishes them in colored boxes. There are two main
types in Fig. 5a using surface 3D point clouds, namely, frog
ornaments with babies and frog ornaments without babies, apart

Fig. 5b shows the clustering results using shape skeletons. The
two main types were the same as those in Fig, 5a. Two groups
in light yellow (i.e., “three-legged with baby”) and red (i.e.,
“three-legged without baby”) are identical to those in Fig. 5a.
For the rest two groups, most instances remained the same,
while A1 was clustered into “without baby” – thus in a different
group. The reason was that the baby on the top of A1 was worn
so that it lost the “baby topological hole/branch” feature in the
first two groups. In addition to the unusual individual A4, C4
was another one for the considerable noises on the two left legs,
which were perhaps surveying errors or worn. Overall, the
clustering results at adaptive threshold = 0.175, as shown in
Fig. 5b, were reasonable and successful. Furthermore, most
frogs in Fig. 5 were clustered in the same four groups, which
were three-legged with baby, four-legged with baby, threelegged w/o baby, and four-legged w/o baby, respectively.

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

296

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

Fig. 5. Dendrogram of hierarchical clustering of the 14 frogs (color box stands for group). (a) Using surface point clouds; (b)
using shape skeletons; (c) Close views of the 3D mesh details
3.2 Comparison results
4. DISCUSSION AND CONCLUSION
Table 2 contrasts the two sets of grouping. Out of the 14
instances, only two instances were inconsistent. The C4 was
grouped as “four-legged with baby” using surface point cloud,
but as “Unusual” using shape skeleton. The A1 was grouped as
“four-legged with baby” using surface, but “four-legged without
baby” using shape skeleton. The TPR = 12/14 = 0.857, which
showed a high-level consistency between the two groups. The
consistency also indicates both surface 3D point clouds and
shape skeletons were appropriate for clustering the 14 frogs and
quantitative comparisons for HDTs and advanced heritage
documentation.
Group using shape skeleton
I
II
III IV V
Group
using
surface
point
clouds

I. Three-legged
with baby
II. Four-legged
with baby
III. Three-legged
IV. Four-legged
V. Unusual

3
1

2
3

3

1
1

Table 2. Confusion matrix of the two clustering results.

Heritage Digital Twins (HDT) and advanced heritage
documentation demand automatic processing methods and
quantitative understandings of unstructured heritage 3D data.
This paper clusters 3D heritage details without training datasets
and annotations. The paper presents an annotation-free
hierarchical clustering method for comparing and grouping
decorative frogs on bronze drums. The method includes a
dissimilarity measurement based on minimum global
registration error and a hierarchical clustering with adaptive
thresholds. A comparative analysis of the dissimilarity measures
with basic surface point clouds and advanced shape skeletons
was conducted to identify the consistency of the two measures.
As shown in Fig. 6a, 12 out of 14 frogs received consistent
(TPR = 0.857) clustering, in four groups plus an ‘Unusual’
group. The 12 clustering results successfully reflected the
morphological analysis of the photo-realistic 3D frog mesh
models, as shown in Fig. 6a. The rest two frogs, however,
received inconsistent grouping using surface points and shape
skeleton. As circled in Fig. 6b, Frog A1’s bay had no
subsidence on its sides, so it was not recognized by the
skeleton; Frog C4’s skeleton was hindered by the geometric

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

297

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

noises around the left forefoot and a small bump on the left hide

Lastly, the patterns on drum tops and curved frog bodies were

Fig. 6. Comparison of the 14 photo-realistic 3D frog mesh models. (a) 12 consistent clustered frogs; (b) 2 inconsistent frogs
leg.

not included.

Overall, the automatic hierarchical clustering method was
proven helpful in processing complex heritage objects, such as
bronze frogs, regardless of using surface points or shape
skeletons. In addition, the consistency in the results pinpointed
new evidence that surface point clouds and shape skeletons are
both appropriate for understanding the (dis)similarities and
groups for HDT and advanced heritage documentation.

Future research directions are recommended as follows. First,
researchers can include and contrast heterogenous frog drums
from other museums in other provinces and countries. A study
of the sampling level of shape skeleton can lead to lightweight
skeleton points, which may be much faster in comparing and
documenting heavy-sized 3D surface point clouds. New robust
shape skeleton extraction algorithms are also recommended to
tolerate inevitable geometric noises in scanned 3D heritage
models.

The annotation-free hierarchical clustering methods can be
applied to other decorative details on cultural relics. Examples
are solar patterns on drum tops, Roman temple columns,
cathedral pillar details, teapot handles, Chinese tie beam
woodcarving, and terracotta tiles. Based on a dissimilarity
equation like Eq. (1), the new 3D heritage details' error matrix
can quantitatively measure the geometrical differences and
automatically group similar decorative details together.
The study in this paper is not free from limitations. First, the
source frog drums were limited to one museum, while the
number of frog instances was limited to 14. Secondly, the shape
skeleton computation adopted from Gao et al. (2015) may be
sensitive to local geometric noises. In addition, the computation
was controlled to be slow – 100,000 sampled points for
mimicking complex objects – for both data presentations.

ACKNOWLEDGEMENTS
The study presented in this study was supported by Natural
Science Fund of Guangdong (No. 2023A1515010757).

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This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

298

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-1-2023
29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage:
Humanities and Digital Technologies for Shaping the Future”, 25–30 June 2023, Florence, Italy

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This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-X-M-1-2023-293-2023 | © Author(s) 2023. CC BY 4.0 License.

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