Low-Cost 3D Reconstruction of Caves

ao Marcelo Teixeira

, Narjara Pimentel, Eder Barbier, Enrico Bernard, Veronica Teichrieb

and Gimena Chaves

Universidade Federal de Pernambuco, Recife, PE, Brazil

Keywords:

RGB-D Sensors, 3D Reconstruction, Cave Surveying.

Abstract:

Caves are spatially complex environments, frequently formed by different shapes and structures. Capturing

cave’s spatial complexity is often necessary for different purposes – from geological to biological aspects –

but difﬁcult due to the challenging logistics, frequent absence of light, and because the necessary equipment

is prohibitively expensive. Efﬁcient and low-cost mapping systems could produce direct and indirect beneﬁts

for cave users and policy-makers, enabling from non-invasive research of fragile structures (like speleothems)

to new forms of interactive experiences in tourism, for example. Here we present a low-cost solution that

combines hardware and software to allow capturing cave spatial information through RGB-D sensors and the

later interpretation of the processed data. Our solution allows the navigation in a 3D reconstructed cave, and

may be used to estimate volume and area information, frequently necessary for conservation or environmental

licensing. We validated the proposed solution by partially reconstructing one cave in Northeastern Brazil.

Although some challenges have to be overcome, our approach showed that it was possible to retrieve relevant

information despite using low-cost RGB-D sensors.

1 INTRODUCTION

Caves are spatially complex tridimensional environ-

ments, mainly composed by irregular shapes. Such

complexity can manifest from a macro (i.e., conduits,

ceiling, walls, ﬂoor) to a smaller scale (i.e., stalactites,

stalagmites and other speleothems of reduced dimen-

sion). Documenting the spatial complexity of caves

is a challenging task, because besides their inherent

characteristics, the frequent lack of light in the cave’s

interior make such task even more difﬁcult. Although

caves have been subject of study for decades, tra-

ditional topography techniques are not always capa-

ble of accurately representing the tridimensional com-

plexity of such environments (Gallay et al., 2015).

Recently, 3D reconstruction techniques have been

applied as an alternative for cave mapping and doc-

umentation. Such approaches are usually based on

laser scanning systems (i.e., Terrestrial Laser Scan-

ning - TLS, Light Detection and Ranging - LiDAR or

Mobile Laser Scanning - MLS) (Ullman et al., 2023)

(Li et al., 2023), where laser beams with thousands of

pulses per second are used to generate point clouds,

which will be recorded together in sequence. Such

approach allows to construct highly detailed 3D rep-

https://orcid.org/0000-0001-7180-512X

resentations of the environment captured, in high res-

olution (dense), enabling a reﬁned view of the cave

(Grohmann, 2019).

The use possibilities of 3D reconstructed repre-

sentations of caves are vast and go beyond scientiﬁc

goals, including from cave documentation to a more

precise estimate of cave volume and area, all use-

ful information for the environmental licensing, eco-

tourism and environmental education. However, de-

spite being very promising, the use of laser scanning

techniques in Brazil is still imature and hindered by

some factors:

• Cost: Laser scanning equipment is very expen-

sive, with prices varying from thousands to hun-

dreds of thousands of Reais (R$);

• Difﬁcult Access: the necessary equipment has

to be imported requiring time-consuming and bu-

reaucratic processes. Moreover, some laser scan-

ning solutions are rated restrict due to military or

security use;

• Handling: Data volume generated by such sys-

tems is usually very large and requires high per-

formance computers.

Such factors make the diffusion of laser-based

3D mapping techniques very restrict in Brazil, lim-

Teixeira, J., Pimentel, N., Barbier, E., Bernard, E., Teichrieb, V. and Chaves, G.

Low-Cost 3D Reconstruction of Caves.

DOI: 10.5220/0011786200003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP, pages

1007-1014

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

1007

iting their access and restricting their use to a few

big companies, some specialized consulting agencies

and an even smaller number of research laborato-

ries (Grohmann, 2019). Such restrictive scenario in

Brazil is very concerning, considering the huge spele-

ological potential of the country, estimated at about

310,000 caves (?). This situation is aggravated by the

lack of information about Brazilian caves and, at the

same time, by the high human pressure they experi-

ence.

A low-cost 3D alternative for cave reconstruction

could increase the possibilities of documentation of

Brazilian caves, contributing to reduce the current in-

formation gap. Moreover, documenting caves and

their characteristics is supported by the current poli-

cies, like the National Program for the Conservation

of the Brazilian Speleological Heritage (item MMA

358/2009). Such initiative provides support for data

production which may help the Brazilian government

to achieve their conservation and research goals. Be-

sides that, protected areas harboring caves could ben-

eﬁt from cave surveying initiatives, enabling, for ex-

ample, non-invasive studies of fragile speleothems,

as well as by new forms of virtual interactions be-

tween tourists and the caves in those areas. Here we

present a low-cost solution that combines hardware

and software to allow capturing cave spatial informa-

tion through RGB-D sensors and the later interpreta-

tion of the processed data for the extraction of addi-

tional data such as cave volume and area.

This paper is therefore structured in the follow-

ing sections. Section 2 brieﬂy describes some related

studies with different solutions for 3D reconstruction

of caves, highlighting the technologies used and the

direct application of the results obtained. Section 3

explains the methodology we used, with the steps for

the proposed process. In Section 4 we discuss the lim-

itations found in the proposed methodology together

with some insights on how to improve the results ob-

tained with the proposed solution. At last, in Section 5

we provide future directions that could make the pro-

posed solution more complete.

2 RELATED WORKS

The work from Oguchi et al. (Oguchi et al., 2011)

divides the main data sources used in earth surface

processes into analogic and digital ones. Analog

data sources comprise text descriptions, hand-drawn

illustrations, analog photographs and videos for vi-

sual interpretation, data from classical ground sur-

veying, topographic data from plane-table and analog

photogrammetry and topographic maps and thematic

maps. Regarding digital data sources, they include

digital ground/aerial photographs and videos for vi-

sual interpretation, digital satellite imagery, digital

aerial imagery, topographic data from modern ground

surveying (GPS, total station, laser range ﬁnder, ter-

restrial laser scanning), analytical and digital pho-

togrammetry, height data from airbone LiDAR and

Airbone/Satellite InSAR, com-piled height informa-

tion and digital topographic maps and thematic maps.

It is important to note that none of the aforemen-

tioned data sources include the use of depth (RGB-

D) sensors. One could argue that Oguchi’s work

was published in 2011, while the ﬁrst low cost main-

stream depth sensor (Kinet V1) was released in 2010.

A different work, from Idrees and Pradhan (Mo-

hammed Oludare and Pradhan, 2016), which presents

the panorama of cave surveying solutions for 10 years

(2006-2016), also does not mention any low-cost ap-

proaches. From the almost ﬁfty works published

in various international journals related to mapping

caves in their true 3D geometry with focus on sen-

sor design, methodology, data processing and appli-

cation development, all of them were based on laser

scanning technologies (time-of-ﬂight and phase shift

ones).

According to Escalera (Escalera, 2012), it is pos-

sible to acquire depth information (to be further used

for 3D reconstructions) using different technologies.

Figure 1 illustrates different depth acquisition tech-

nologies, highlighting the light wave-based ones that

do not make use of laser measurements (in other

words, the ones that are low cost alternatives). The so-

lution proposed in this work makes use of an RGB-D

sensor based on the emission of structured light (cam-

era + light pattern projection) to infer depth informa-

tion, the ASUS Xtion PRO

. By performing a search

in Google Scholar using the “3D reconstruction of

caves” string, we noticed that most works found make

use of laser scanning to acquire cave data (Gallay

et al., 2016) (Pukansk

a et al., 2018) (Gallay et al.,

2015) (Beraldin et al., 2006) . Despite this tendency,

the work from Sellers and Chamberlain (Sellers and

Chamberlain, 1998) uses ultrasound reﬂections while

the work from Lee (Lee, 2018) presents the use of

RGB cameras (photogrammetry) as a low-cost alter-

native to the reconstruction of caves, but the work is

mostly focused on underwater caves.

Additionally, some works utilize more than one

type of sensor at the same time to improve data ac-

quisition (Azp

urua et al., 2023). This happens in the

work of MacFarlane et al. (McFarlane et al., 2013),

in which they combine data acquired from aerial

photogrammetry using an autonomous drone, three-

https://www.asus.com/3D-Sensor/Xtion PRO

VISAPP 2023 - 18th International Conference on Computer Vision Theory and Applications

1008

Figure 1: Different technologies for the acquisition of depth maps. Modiﬁed from (Escalera, 2012).

dimensional cave laser scanning at millimeter resolu-

tion, differentially corrected geodetic GPS, and con-

ventional compass-based cave surveying techniques.

The applications resulting from cave reconstruc-

tions go from preservation of the caves themselves

(Aiello et al., 2019), to make them accessible to dis-

abled people that cannot visit them “in loco” (Tomet-

zov

a et al., 2020) and art (Idrees and Pradhan, 2017a).

Besides that, it is possible to obtain valuable informa-

tion such as the cave volume and channel surface area.

3 METHODOLOGY

Independently of the data acquisition technology

used, the methodologies used for capturing data in-

side the cave and visualizing the ﬁnal post processed

reconstruction are very similar. Figures 2, 3, 4 and 5

illustrate some of them.

The methodology we adopted can be simpliﬁed in

three macro stages: data capture, 3D reconstruction of

the cave, and visualization/extraction of information

based on the generated point cloud.

3.1 Data Capture

The state-of-the-art in capturing tridimensional data

(specially for caves) is focused on laser scanning

technologies (Grohmann, 2019). The problem with

such technologies is that their cost is directly propor-

tional to the quality of the obtained results. A high-

resolution laser scanner/LiDAR may reach dozens of

thousands of dollars, which restricts their acquisition

and use. Low cost alternatives do exist, and it is pos-

sible to obtain equivalent results with the use of pho-

togrammetry (RGB-only) or low resolution RGB-D

sensors, by combining many captured images. We

Figure 2: Data processing and analysis workﬂow from the

work of Idrees and Pradhan (Idrees and Pradhan, 2017a).

analyzed several low cost RGB-D sensors focusing

different aspects such as size, weight, available APIs,

power consumption, resolution and ﬁeld of view. The

ten different sensors analyzed were: Orbecc Astra,

Orbecc Astra Pro, Microsoft Kinect V1, Microsoft

Kinect V2, Asus Xtion PRO, Realsense D435, Re-

alsense F200, Realsense SR300, Realsense R200, Re-

alsense ZR300. While both Kinect models allowed

very good reconstruction results, they need an exter-

nal power source, which makes their use difﬁcult in

the ﬁeld, inside caves. The Asus Xtion PRO showed

similar quality to Kinect V1, with the advantage of no

necessity to an external power source, and that was

the reason behind it was selected for our work.

The sensor was attached to a MacBook Air (11

inches version) with 4GB RAM, 128GB SSD drive

and a Core i5 1.4GHz processor. Despite being a

computer with more than ﬁve years, its battery still

keeps it running for about 2 hours, enough for data

capture. External drives were also used to free Mac-

Low-Cost 3D Reconstruction of Caves

1009

Figure 3: General workﬂow diagram from the work of

Tometzova et al. (Tometzov

a et al., 2020).

Figure 4: Methodological workﬂow diagram from the work

of Idrees and Pradhan (Idrees and Pradhan, 2017b).

Book’s storage space. In the future, we intend to use a

more portable and dedicated solution such as a Rasp-

berry Pi 4.

For performing both capture and processing of

the data, the Real-Time Appearance-Based Mapping

(RTAB-Map

) was used. It is an RGB-D, Stereo and

LIDAR Graph-Based SLAM software based on an

incremental appearance-based loop closure detector.

RTAB-Map being a loop-closure approach with mem-

ory management as its core, is independent of the

odometry approach used, meaning that it can be fed

with visual odometry, LIDAR odometry, or even just

wheel odometry. This means that RTAB-Map can be

used to implement either a visual SLAM approach, a

LIDAR SLAM approach, or a mix of both, making

it possible to reconstruct a 3D point cloud based on

RGB-D data.

Using the aforementioned hardware/software

combination, three biologists went to ﬁeld-test the so-

lution in a real cave (Carrapateira), located at the mu-

nicipality of Felipe Guerra, in Rio Grande do Norte

state, northeastern Brazil. They started scanning the

cave’s entrance with RTAB-Map’s main application

running, capturing the data and viewing in real time if

image reg-istration was working. The entire cave was

scanned in parts and the output for each capture ses-

sion corresponded to a .db ﬁle, which is an SQLite ﬁle

format that stores RGB and depth information, and

will be later used in the 3D reconstruction process.

3.2 3D Reconstruction

The RTAB-Map’s 3D reconstruction pipeline can be

fed with real time data from sensors, or with im-

age frames of a previously recorded database, which

was used in this work. The default options were

used and the only modiﬁcation was the feature detec-

tor/descriptor chosen, set to SIFT (Lowe, 2004). In

order to perform the reconstruction process, a more

powerful computer was used. Even using a computer

with a Core i7 CPU 2.9GHz processor, with 32GB

of RAM memory, 2TB SSD storage and an NVIDIA

GTX 1080 GPU, the entire reconstruction takes more

than 15 minutes to be completed.

Since the cave data was captured by parts, i.e.,

there were different .db ﬁles corresponding to the

same cave, it was possible to split the reconstruction

process, processing each ﬁle at a time. RTAB-Map

is capable of receiving all .db ﬁles at once and pro-

cess all of them together. In fact, we tried that option,

but the reconstruction took too long and the software

was not able to correctly register the different point

clouds captured. Therefore, we opted to generate a

point cloud for each .db ﬁle captured and later per-

form a manual registration of all point clouds.

http://introlab.github.io/rtabmap

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Figure 5: Protogrammetry workﬂow from the work of Lee (Lee, 2018).

3.3 Data Extraction/Visualization

An important part of the full 3D reconstruction of a

cave lies in the combination of its point clouds. If

there is more than one point cloud to process, a reg-

istration operation is necessary, i.e., an alignment be-

tween different points clouds will be necessary and

special attention must be paid to precisely overlap in-

tersections. This alignment can be done manually or

automatically, and this is a computationally demand-

ing process since its goal is to ﬁnd a 3D to 3D trans-

form that brings the points from a point cloud coordi-

nate system to a different one, in a coherent way.

After obtaining the isolated point clouds, we used

Meshlab

for the manual point cloud registration.

This process was done in the presence of the persons

that captured the original data, so they could solve

questions about the cave shape. Figure 6 illustrates

the process of manually registering two point clouds

using Meshlab. While one point cloud remains still,

the second one is rotated/moved to match the previ-

ous one. Since we use an RGB-D sensor to capture

data, and their reference scale is the same, there is

no need to change scale for all points clouds. Some-

times, photos taken inside the cave were used to make

sure the point clouds being registered was correct. An

example of such photos can be visualized in Figure

7. In future situations, using some landmarks (e.g. a

solid object set at the cave ﬂoor or walls) when cap-

turing data would make this matching process easier

and more precise. After performing registration for

all generated point clouds, we were able to see the

entire path reconstructed, as shown in Figure 8.

After generating the ﬁnal point cloud based on

the combination of the partial ones, data visualization

https://www.meshlab.net

is a concern. Considering that in the case of caves

points clouds will be usually dense (made of millions

of points close to each other), optimized approaches

must be used to avoid wasting the available computa-

tional resources. Idrees and Pradhan (Idrees and Prad-

han, 2017b) describe different possible visualizations

for data captured.

Figure 6: Example of two different point clouds being man-

ually registered using Meshlab.

4 DISCUSSION

3D mapping has been a valuable support tool in sev-

eral areas but just recently it became accessible for

speleologists and other cave-related professionals. 3D

mapping may help those professionals to acquire reli-

able information on the cave’s physical structure, and

even on biological features, like deposits of guano or

areas with higher animal concentration. However, this

is a relatively new technology and many cave profes-

sionals are not familiar with, so capturing such data

in ﬁeld conditions may not be a simple task. The

major difﬁculties may be associated to the correct

equipment manipulation, good quality illumination

Low-Cost 3D Reconstruction of Caves

1011

Figure 7: Photo taken by biologist to illustrate an interior

passage of the cave.

and even to how well- trained are the professionals to

capture data. All these characteristics are fundamen-

tal for an adequate data registration. Initial patience

and training are necessary.

The equipment used (Macbook + Xtion sensor)

showed to be very sensible to rapid movements and to

abrupt direction changes during the scanning process.

In situations like that, the software used for capturing

the data (RTAB-Map) frequently freezes due to im-

precise tentatives to ﬁnd consecutive matched frames.

When the baseline is too large, the frames are differ-

ent enough to result in less matches, and consequently

the slam algorithm used in RTAB-Map failed. More-

over, there are difﬁcult parts of the cave to access due

to their irregularity, making it harder for the team of

biologists to reach them.

The need for regular illumination and the use

of the sensor coupled to the laptop demanded more

than one professional for capturing the data. In

our approach, three professionals fully equipped with

lanterns and reﬂectors were required to work syn-

chronously so that the data capture could be success-

ful. In order to shorten the wide baseline between

frames, the ﬁeld team started moving as slow as pos-

sible. One suggestion was to adapt the equipment to

a stabilization system to minimize the impact caused

by the terrain irregularity. The biologists were capa-

ble of using the equipment (Macbook + Xtion), but

they were just testing it. Reﬁnements will be nec-

essary to improve both usability and ergonomics, in

case a better precision is needed.

Considering a laptop is used for visualization of

the captured data in real time, the cable connecting

the laptop to the RGB-D sensor has to be long enough

so the ﬁeld team can keep distance while capturing

data inside the cave. A shorter cable would make the

capturing process less continuous and unstable, con-

tributing to the interruption of the process.

The proposed approach may generate different

database ﬁles for the same physical space (with much

data intersection). While this is good for registering

the point clouds, it is advisable to use some kind of

marker on the cave in order to facilitate further man-

ual registration of the captured point clouds. In this

work, the capture was performed without the use of

such visual markers, and it made the manual registra-

tion process take longer than expected.

Battery availability for the notebook used in ﬁeld

work may also be an issue. A good battery auton-

omy may allow a single shot mapping, depending on

the extension of the cave and the objective of the ﬁeld

team. Since our ﬁeld team was testing the solution

and wanted to evaluate the quality of the captured

data, they had no problem in this sense. However,

longer and deeper caves will certainly require mul-

tiple scanning sessions or frequent battery replace-

ments.

During our ﬁeld test, the system was freezing af-

ter some minutes of use due to memory restrictions

of the notebook used. We suspect that this happens

due to RTAB-Map’s high memory consumption dur-

ing the capture. Our ﬁeld team recommend that caves

with larger spaces should be scanned in different parts

so that the point clouds could be joined later. The

longer is the capture time, the higher is the possibility

of the software to freeze and to lose the captured data

so far. Therefore, the ﬁeld team must previously an-

alyze which parts of the cave will be recorded before

moving to a new section.

In summary, the equipment proved to be satisfac-

tory in the sense of allowing the acquisition of data

needed, but there is still room for improvements in

both hardware and software.

In the software side, we are currently working

on porting RTAB-Map to the Android platform while

adapting it to work with Intel Realsense RGB-D sen-

sors. This way, any Android phone could work as

a portable capture/reconstruction station. There is a

RTAB-Map version for iOS devices that have LiDAR

sensors, and we intend to do the same with Android

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Figure 8: Final reconstructed point cloud obtained from manual registration using Meshlab.

devices that use external RGB-D sensors. We also in-

tend to do some comparison of the obtained results

with the 3D reconstruction that comes as native out-

put of an iPhone 14 pro max, since it already has a Li-

DAR sensor and provides 3D reconstruction capabil-

ities natively. We have performed some experiments

on controlled environments with the iPhone and the

results were quite impressive, but tests in the real cave

scenario are still being planned. Efforts regarding im-

proving the reconstruction processes are also on the

go. We are performing some experimentation with

Neural Radiance Fields (NeRFs), which seems to be

the state of the art in 3D reconstruction at the time this

paper was written. Even for NeRFs, the cave environ-

ment may be challenging due to lack of or irregular

illumination and should be carefully investigated.

5 CONCLUSION

This work proposed a low-cost solution for 3D recon-

struction of caves, by means of the use of RGB-D sen-

sors. Our approach is an interesting alternative to the

well-known and commonly used laser scanning tech-

nologies, which are prohibitively expensive and hard

to acquire in Brazil. The proposed approach should

allow cave users to scan parts or even complete cave

structures, with emphasis on smaller parts like spe-

ciﬁc speleothems or details. The used sensor showed

enough resolution to enable a full 3D reconstruction

based on the combination of a sequence of captured

RGB-D images. Our proposed solution could en-

able, for instance, the non-destructive documentation

of cave shapes, forms and structures with the 3D mod-

els being manipulated from different points of view

in the computer. Besides, the 3D maps produced may

complement, correct and reﬁne already existing topo-

graphical and cartographic mappings or new ones in

unmapped caves.

Complementary, information such as volume and

area calculations from the ﬁnal cave 3D point cloud

may be used for additional research and environmen-

tal licensing. As future directions, we intend to de-

velop a miniaturized hardware platform based on a

Raspberry Pi 4 so it would be possible to have higher

mobility while capturing data inside the caves. This

would solve some of the problems detected by us

in the ﬁeld. We also intend to improve the cap-

ture process by using a customized data recorder tool

from RTAB-Map. This would allow less processing

while capturing data, and consequently less power

consumption.

Finally, we intend to improve the 3D reconstruc-

tion process so that less human intervention is nec-

essary to record the point clouds. We plan to de-

velop an optimized solution to allow cave visualiza-

tion via web browser, using for example the Three.js

library. However, this will be challenging due to the

high number of points present in ﬁnal caves recon-

structions. Optimizations will be necessary in order

to allow real time navigation on the browser inside

the virtual caves. Comparison with existing methods

regarding performance is also planned in a near fu-

ture.

ACKNOWLEDGEMENTS

The authors would like to thank CECAV/ICMBio

for ﬁnancially supporting project “Modelagem 3D de

cavidades naturais subterr

aneas”.

https://threejs.org/

Low-Cost 3D Reconstruction of Caves

1013

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