1A Method of Crowdsourced Task Request
Optimization for 3D Reconstruction in Urban Space
Shinya Akiyama† , Yuki Matsuda† , Hirohiko Suwa† , and Keiichi Yasumoto†† Nara Institute of Science and Technology, Nara, Japan,{akiyama.shinya.aq9, yukimat, h-suwa, yasumoto}@is.naist.jp

Abstract—This paper proposes an optimization method forscanning task requests to reduce various costs involved in creating
and updating 3D models. In recent years, there has been agrowing interest in research related to metaverses. Among them,
the utilization of 3D models created by scanning real-world buildings has various applications. Examples include virtual travel
experiences and simulations for autonomous driving. However,the creation of 3D models is time-consuming and costly, Asa result, the updating of 3D models occurs on a time scaleof several months to several years. However, due to the low
frequency of updating 3D models, virtual travel experiences canbe unsatisfying compared to real-world travel. For example,
objects related to seasonal events or foliage changes in trees
may not be updated, resulting in a lack of immersion in thevirtual travel experience. In this study, we propose a method
to induce (request) general participants interested in the target
object to take photos that are useful for 3D model creation andupdating. The goal of this research is to increase the frequencyof creating and updating 3D models, thereby achieving a more
realistic metaverse experience.
Index Terms—modeling, photogrammetry, scanning task

I. I NTRODUCTION
In recent years, there has been a surge of research in the ﬁeldof metaverse. Among them, a particularly noteworthy topic isthe endeavor to recreate real-world cities within virtual spaces
by placing 3D models of buildings and objects that are scanned
from the physical environment. This holds great potential invarious areas, such as virtual tourism, where users can explore
virtual cities as if they were in the real world, urban planning,
simulations like autonomous driving, and more. However, it isimportant to note that the objects required for these endeavors
are not limited to static structures like buildings. In realworld travel, one can experience the differences brought byseasonal changes by visiting the same destination repeatedly.
This is a common way of enjoying travel. However, due
to the low frequency of updating 3D models, virtual travel
experiences can be unsatisfying compared to real-world travel.
For example, objects related to seasonal events or foliagechanges in trees may not be updated, resulting in a lack of
immersion in the virtual travel experience.
In this paper, we propose a participatory sensing system fortimely 3D modeling of objects referred to as “Urban Objects”
that are either installed for a limited period or require relatively
frequent updates compared to annual updates. The system
is designed to enable participants to complete sensing tasks
using widely available devices without requiring specializedknowledge. The goal is to reduce scanning costs, improve theaccuracy of 3D model creation, and increase the frequency ofupdates.
II. P HOTOGRAMMETRY: R EQUIREMENTS AND R ELATED
W ORK
A. Requirements
To bring “Urban Objects” into the virtual space, it is necessary to complete the scanning of the target objects within arelatively short period while they exist in the real world. Thereare various methods for 3D modeling, but photogrammetry isa technique that can be used to quickly generate 3D models,
including surface textures, without speciﬁc focus on the texture
itself. Photogrammetry involves capturing photographs of thetarget object from various angles and combining them togenerate a 3D object. However, one challenge of photogrammetry is that generating a seamless 3D model requires a large
number of photographs taken from different angles to avoid
any inconsistencies or artifacts.
To assess the required angular resolution for the targetobject, a dataset of 36 photographs was prepared, capturing theobject from a ﬁxed distance at 10-degree intervals, covering
a complete 360-degree rotation. The ObjectCapture software,provided by Apple, was used to attempt 3D object reconstruction. The results provided insights into the photos that areuseful for photogrammetry.
• Each photograph should capture both the same anddifferent parts of the target object.
• If the above conditions are met, it was possible togenerate a 3D object even from just three photographs.
• With a complete dataset covering a full 360-degree
rotation of the target object, it is possible to generatea 3D object with sufﬁcient realism.
• The parts not captured in the photograph are represented
as missing in the 3D object.
• The image quality of the photographs directly affects therealism of the generated object.
B. Related Work
Structure from Motion (SfM) [1] is one of the representative
methods in photogrammetry. SfM extracts feature points fromnumerous photographs of a target object and connects them togenerate a 3D model. SfM also allows for the estimation ofrelative positions at which the photographs were taken withrespect to the object. This has been applied in research efforts

2Fig. 1: Proposed Method

such as integrating sets of photographs of buildings from theinternet into 3D maps to facilitate browsing and searching [2].
There are also previous examples of utilizing SfM to suggest
unphotographed locations on maps and encourage players tocapture photos of those places in gaming scenarios [3].
III. P ROPOSED M ETHOD
In this paper, we propose a task assignment approach thatencourages event participants and tourists to naturally capture
the photos required for photogrammetry. While this approachshares similarities with the study conducted by Tuite et al.
[3], the key difference lies in the photographers themselves.
Instead of targeting speciﬁc players in a game, our approachaims to involve general participants such as event attendees
and tourists who encounter “Urban Objects” in the city. The
objective is to encourage a wide range of participants tocapture photos.
This method of requesting photography tasks is intended
to be integrated into a 3D object generation service. This
3D object generation service possesses the characteristics of
low-cost 3D generation and frequent object updates, making
it widely applicable to the conventional needs of 3D object
generation. For instance, it can be utilized in contexts such as
content creation for movies, games, and virtual tours, simulations for urban planning, or the creation of miniatures using
3D printers. Our proposed method consists of three phases,
but it is premised on the initiation of requests for 3D object
generation from individuals who require 3D objects with theaforementioned needs. Individuals who wish to create 3D objects are free to determine their budget for object creation, andour envisioned service determines the amount of compensation
based on their budget and the contributions of participants.
This compensation can be in the form of cash, souvenir
coupons, in-game currency, or items that photographers ﬁnd
valuable̶ anything that holds value for photographers. Thismechanism itself is akin to crowdfunding.
The overview of the proposed method is illustrated in Fig. 1.
In the ﬁrst phase, event participants or tourists capture
photos of the target objects in a spontaneous manner andupload them to the system. These photos are taken with a senseof taking commemorative photos and are considered sufﬁcient.
Regarding the uploaded photos, a uniform reward will be given
to the uploader for each uploaded photo.
Next, using Structure-from-Motion (SfM), the collected setof photos is used to generate 3D models of the captured

objects, and the locations where the photos were taken aredetermined.
The second phase is the evaluation phase. Participants who
have uploaded photos are sent links to the 3D models and areasked to identify any missing or unnatural parts of the models.
Participants who provide such feedback are rewarded with amodest compensation. Based on the feedback, photos that areneeded for capturing the missing parts (e.g., higher-resolution
photos or photos from speciﬁc angles) are determined. Additionally, using the calculated shooting locations from the setof photos obtained in the ﬁrst phase, angles or points where
the objects have not been captured are identiﬁed and matchedwith the photos determined based on the feedback.
The third phase is the task assignment phase. In this phase,
individuals present in the vicinity of the object are assigned thetask of capturing the required photos identiﬁed in the second
phase. This is done by placing the 3D model created in theﬁrst phase on a map that can be viewed on a smartphone.
Participants can select the model as an icon and view the“capture task” associated with it. The capture task includes
information such as the duration of the task, desired weather
conditions, image quality, and speciﬁc shooting angles. Participants capture photos according to the task requirements andupload them. The system evaluates the task completion rate of
the uploaded photos using mathematical criteria and rewards
the photographers based on their achievements. The rewards
offered for task completion are set relatively higher than thoseobtained in the ﬁrst and second phases.
Afterwards, the second phase and the third phase are essentially repeated until one of the following conditions is met:
the number of identiﬁed issues in the second phase falls below
a certain threshold, the client’s budget is depleted, the clientis satisﬁed with the quality of the 3D object and chooses toconclude the request, or the removal of the object is reported
within the second phase.
IV. C ONCLUSION
In this paper, we propose a method for increasing theupdate frequency of 3D models of objects at a low cost by
widely outsourcing scanning tasks to general users. In future
research, we plan to validate the effectiveness of this proposed
method within a 3D object generation service, focusing on
streamlining and cost reduction in 3D model updates.
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Vision, 2nd ed. Cambridge University Press, 2003.
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no. 3, pp. 835–846, 2006. [Online]. Available: https://doi.org/10.1145/11
41911.1141964[3] K. Tuite, N. Snavely, D.-y. Hsiao, N. Tabing, and Z. Popovic, “PhotoCity:Training Experts at Large-Scale Image Acquisition through a Competitive
Game,” in Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems, ser. CHI ’11, 2011, pp. 1383–1392. [Online].
Available: https://doi.org/10.1145/1978942.1979146