MODEL_FOR_SEMANTICALLY_RICH_POINT_CLOUD.pdf

MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA
F. Poux a, †, *
, R. Neuville a
, P. Hallot b
, R. Billen a
a
ULG, Geomatics Unit, University of Liège, 4000 Liège, Belgium- (fpoux, romain.neuville, rbillen)@ulg.ac.be
b
ULG, Architecture, LNA-DIVA, University of Liège, 4000 Liège, Belgium- p.hallot@ulg.ac.be
†
Main author and contributor
KEY WORDS: point cloud, data model, classification, segmentation, point cloud database, semantics, 3D spatial data
ABSTRACT:
This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds
from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an
exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but
processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced
smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available
knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially
many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and
allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.
1. INTRODUCTION
Among the ever-increasing flood of data collected every day,
geospatial data occupies a large portion. While the well-known
sentence “80% of the data is geographic” (Hahmann et al., 2011)
is an arguable empirical statement, it delineates a need to find
new or improved ways to derive value from big geospatial data
(Li et al., 2015). The expansion’s potential to benefit many
applications such as construction, emergency response, planning,
monitoring critical infrastructures and transportation makes it a
favourable way to address many societal problematics.
Extracting information from spatial concepts help us better
understand our world and take informed decisions. Although
geospatial data “stricto senso” is the bedrock of many decision-
making processes, the injection of semantics enhances the
representativeness of the data while enlarging possible
applications. GIS research targets this enrichment by finding
solutions to make the storage, retrieval, and manipulation of
spatial data easier and more representative.
Since a decade, computing power is advanced enough allowing
GIS systems to be extended for managing 3D models hosting
metadata. The Open Geospatial Consortium (OGC) work and
standards has brought stable references that are adopted
internationally. The most used example for planning and
construction is CityGML, IndoorGML, as well as BIM-IFC ISO
standards, which allows many applications that 3D geospatial
data alone (i.e meshes, CAD) could not answer. But these models
are derived from a more direct source of information, and as our
means to capture all 3 dimensions of our world evolve, we
increasingly deal with point clouds from LiDAR, TLS1
, MLS2
,
HMLS3
, MMSS4
and dense image-matching. They constitute
geospatial data that are gradually more produced through various
*
Corresponding author.
1
Terrestrial laser scanner
2
Mobile laser scanner
3
Hand-held mobile laser scanner
4
Backpack-mounted mobile scanning system
means and platform, but their democratization doesn’t follow the
same growth curve. Indeed, on top of the captured 3D geometry,
each application specifically requires additional semantics often
domain-linked to allow a direct exploitation from the collected
information. For this reason, it is highly impractical to solely base
reasoning on point cloud spatial attributes alone.
Our paper aims at solving this issue to allow a straight integration
and a better interfacing of point cloud data in our computerized
environment. On top of an efficient data management system that
can handle the ever growing data size, complexity and
heterogeneity, the addition of knowledge to such a structure is
very interesting for meaningful information integration (Poux et
al., 2016a). Enabling semantic injection to point cloud is a first
part in undertaking the creation of intelligent environments
(Novak, 1997) to allow digital copies of the world to be used as
decision-making tools. However, this demands a highly
functional reasoning engine that allows both spatial and semantic
queries to efficiently interpret natural language and spatial
operations. However, the needed metadata are “typically drawn
from distributed sources and often are thematically and spatially
fragmented. Thus, for a given geographic region, data differ in
quality and modelled semantic aspects” (Stadler and Kolbe,
2007). Therefore, spatio-semantic coherence is mandatory to
enable a valid and representative modeling of the environment.
This article is based on the previous work of (Poux et al., 2016a),
which defines a Smart Point Cloud (SPC) workflow that enables
a connection between point cloud data and three identified
knowledge sources being device, analytic and domain
knowledge. We extend this concept by proposing a conceptual
data model composed of three meta-models acting at three
different conceptual levels to efficiently manage massive point
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W5, 2017
12th 3D Geoinfo Conference 2017, 26–27 October 2017, Melbourne, Australia
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-IV-4-W5-107-2017 | © Authors 2017. CC BY 4.0 License. 107

cloud data (and by extension any complex 3D data) while
integrating semantics coherently.
In the first part, we will review the use of semantics for
enhancing 3D data regarding domain applications and existing
standards for managing multi-dimensional geospatial data. Then
we will present the extended modular Smart Point Cloud model
to allow a more direct interoperable integration of semantics in
point-based virtual environments. Finally, we will discuss and
illustrate the proposed conceptual model by a prototype in
python and PostgreSQL to manage an indoor point cloud
captured by a terrestrial laser scanner, enabling reasoning from
information extraction.
2. SEMANTICS & 3D DATA
Communities working with 3D data are very different implying
a wide diversity on how the data is used. This explicitly demands
that data-driven applications enable targeted information
extraction specific to each use case. For example, if you work
with a mesh representing what looks like a table, while you know
it is a table, you need to digitally attach some extra information
if you want to deepen the operations made by the computer rather
than interpreting on the fly (brain work). Indeed, once metadata
is attached to 3D data, then you can more easily grasp, or even
can make calculations that were impossible before (e.g. count
the number of table in the scene, what is the surface size of each
table …). These are very basic examples of how metadata
enhances the use of 3D data. But of course, while this is rather
convenient for one use case, making a general rule that applies
to all models is a daunting task. Each application requires its
own semantic and geometric information. In this first part, we
will study the attempts, standards and existing reflections to
define a common scheme for exchanging relevant 3D
information.
2.1 Explicit 3D spatial information systems
Datasets that explicitly include spatial information are typically
distinguished regarding the data models and structures used to
create, manage, process, and visualize the data. Ee consider
three analogically distinct categories of 3D data environments
(Ross, 2010):
- 3D GIS: GIS systems typically model the world itself,
retaining information about networks, connectivity,
conductivity, associativity and topology. This enables
spatial analysis, often carried on large collections of
3D instances stored in virtual data warehouses with
coordinates expressed in a frame of reference.
- 3D CAD: CAD/CAM techniques model objects in the
real world through parametric and triangular
modelling tools. The topology is often planar or
limited (although vendors extend functions to include
semantics and higher descriptive topology (Zlatanova
and Rahman, 2002)) and the retained information
usually plays on a visual scale. The distinction
between storage and visualization is not as well
defined as in 3D GIS systems, and typically files are
stored as single complex 3D objects (one file). CAD
files carry visualization information that is not
relevant to the data itself. A simplistic difference
consists in thinking of 3D GIS systems as 3D spatial
database whereas 3D CAD models are rather related
to 3D drawings. The coordinate system is therefore
linked to a defined point of interest (often the
centroid) in the scene.
- BIM: it constitutes working methods and a 3D
parametric digital model that contains “intelligent”
and structured data initially for planning and
management purposes. It is often studied for its
integration with 3D GIS systems with an extensive
review in (Liu et al., 2017), but their parallel
evolution (conditioned by temporal and hermetic
domain research) and fundamentally different
application scopes are slowing down their common
assimilation. BIM models share many properties with
3D CAD models, including their expression of
coordinates in a local system, but benefit of a higher
semantic integration.
The emergence of new data sources and evolution in data models
constantly put in question the suitability of these categorizations.
Established and emerging data types and their integration /
characterization can become difficult for meeting the
characteristics of one of these categories. For example, a more
primal spatial data from a more direct data source such as 3D
point clouds could benefit of their own category. Indeed, they
have a very small direct integration in these groups, but rather
serve as a support for the creation of CAD/CAM models, BIM
models or 3D GIS systems. In some advanced cases, the
information included in 3D point clouds can help extract
metadata for the future data model.
However, it is important to note that while barriers between each
category was well defined five years ago, the improvement and
added functionalities to each category as well as interoperability
and integration research and standardization plays a major part
into blurring the respective frontiers.
2.2 Interoperability and ontologies for 3D semantically-rich
data
“Semantic interoperability is the technical analogue to human
communication and cooperation” (Kuhn, 2005). This sentence
pertinently summarize the drive in GIS research to formalize
semantics in order to facilitate the communication of data among
different communities. Different levels of interoperability exist,
and we are looking in this paper at the technical parts without
looking at societal issues raised by enterprise-oriented
information sharing (Harvey et al., 1999). However, the
conceptualization of interoperability in our computerized
environment remains a challenge at different levels:
1. The nature of concepts that defines interoperability
should not arise from simplistic assumptions as
notions evolve with time;
2. The ever growing use of 3D data makes it very hard
to define a common language to be spoken by all
professionals;
3. The knowledge involved is sparse enough to
constraint natural language extension in a
computerized formalism;
4. Standardization efforts need an international
cooperation to represent as thoroughly as possible the
reality and benefit of effective coordination;
5. Retaining semiotic relationships between concept,
symbol and entity, as in the semantic triangle (Ogden
et al., 1923).

In a narrower context, 3D data as 3D models are largely used for
a high number of applications, which vary in scope and scale as
well as enlargement. Therefore, semantic schemes as generic as
possible provide a potential solution for interoperability.
Ontologies are a good way to explicitly define knowledge in
order to address semantic heterogeneity problematics arising
from this large variety. However, independent work and research
limits their extension to a broader audience especially looking at
3D content. But the rise in usage demands that specific solutions
allow 3D data to be exchanged and used as thoroughly as
possible. Independent development and uncoordinated actions in
the research field of ontologies applied to GIS are addressed by
entities such as the World Wide Web Consortium (W3C), the
International Organization for Standardization (ISO), the Open
Geospatial Consortium (OGC), the International Alliance for
Interoperability (IAI) and the rise of open-source developments
and repositories. Clarifying standardization processes over 3D
data is especially important, with issues arising at both a technical
level and a consideration level (how is 3D data considered by the
community?)
In general, a standard defines a data model at two levels:
properties and geometry. A well-known example is the standard
GML3 issued by the OGC which is used by the CityGML data
model describing the geometrical, topological, and semantic
aspects of 3D city models (Kolbe et al., 2005). The specification
and the decomposition in Level of Details (LoD) as well as the
current 2.0 version allowing to define semantic concepts has
made the integration of city models easier and applicable to a
wider range of use cases (Biljecki et al., 2015). Indeed, this gives
the possibilities for decision makers to impose a specific
“abstraction figure” (LoD1, LoD2 …) that characterizes the
granularity level of the wanted geometry and semantic concepts.
This interoperability “tool” is a leap forward in the
democratization of the standardized data model CityGML.
However, its integration with other standards or ontologies is still
being discussed and studied, where a discrete number of LoD
with ‘unconnected’ (potentially uneven) levels could be a
concern (Karim et al., 2017). This illustrates the need to find
interoperable systems between already established standards to
benefit of higher semantics and topology integration that
enhances our comprehension and usability of 3D data.
The Semantic web is a great tool standardized through Semantic
Web 3.0 that is able to create links between already established
standards, which encourages the use of web-based data formats
and exchange protocols, with the Resource Description
Framework (RDF) as the basic format. Indeed, this has the
potential to greatly reduce the gap/frontier between each
previously defined category in 2.1, and better integrate
knowledge within 3D spatial data. This is especially efficient if
we better integrate 3D point clouds, on which we today derive so
many systems and data models. Indeed, in a first time it could
serve as transition data, but given time it could provide all the
necessary information if correctly integrated.
2.3 Transition to point clouds
The work of (Janowicz et al., 2010) constitutes a pertinent
analysis of identified interoperability problems and semantic
enrichment routines. The authors outline semantic challenges for
geospatial applications, namely discovery, access, registration,
processing and visualisation. They propose a Semantic
Enrichment Layer which includes interesting functionalities, in
particular a reasoning module to load and query specific
ontologies.
Semantics injection into point cloud constitutes an opening to
gather interoperable data with a reasoning potential. It relates to
complex and active point cloud research vigorously driven by
governmental, industrial and academic needs, such as sustainable
planning, self-driving cars or VR teaching. It includes
problematics that arise from point cloud data management in
terms of storing and efficient structuration for semantic and
spatial demands. Indeed, existing PCDBMS (point cloud
database management systems) and indexing techniques provide
a solution to storing, compressing and managing the data (Dobos
et al., 2014; Richter and Döllner, 2013; van Oosterom et al.,
2015), but efficiency and extensibility to dynamic semantic
update and ontological reasoning stays limited. Queries over
octree derived indexing techniques can provide an efficient
solution for out-of-core rendering and parallel processing, but
data structuration cannot efficiently include context adaptation
and inference reasoning.
Therefore, identifying links and relations within segmented
objects becomes essential to truly understand how each spatial
entity relates to its surroundings and connecting GIS, CAD and
BIM concepts (as seen in 2.1) to 3D point clouds. Certain
approaches such as (Ben Hmida et al., 2012a) and (Ben Hmida et
al., 2012b) provide an opening on domain knowledge integration.
An important contribution is made by (Rusu et al., 2008), to
turn a kitchen point cloud – and by extending any environment
– into a meaningful representation for robot interaction and
recognition called an object-map. Their algorithm includes data
acquisition, geometrical mapping and functional mapping.
Geometrical mapping is composed of an outlier removal, a
persistent feature estimation, a point cloud registration and then
a resampling step. Building on this, a segmentation process and
model fitting based on the geometrical mapping constitute the
basis for region identification and object hypotheses
constituting the functional mapping. As an end product, the
algorithm produces a mesh via functional reasoning. It is
interesting to note that the intangible hierarchy representation
allows to validate segmentation and add constraints to refine the
results. Although the concept is a first step toward topological
concepts, this paper bases functional reasoning on common-
sense knowledge mostly hardcoded.
While these constitute pertinent examples of knowledge and
semantic enhancing capabilities, no clear and defined structure is
developed. The work of (Poux et al., 2016a) is a first step in this
direction: it proposes a global framework that classify, organise,
structure and validate objects detected through a flexible and
highly contextual structure that can adapt to three identified
knowledge sources being domain, device and analytic
knowledge. This lays the groundwork for the development of a
new data model – the smart point cloud – that can address
previously identified issues while retaining a high level of
interoperability with existing standards.
3. THE SMART POINT CLOUD
(Poux et al. (2016a) propose a definition for a point cloud
knowledge-based structure contextually subdivided according to
classification results. The semantization process relies on
geometrical descriptors as well as a domain analogy integrated in
a new structuration of the point cloud data through correct
indexing techniques. This implies a separation between
relationships / topology and spatial / attribute information to
provide efficient data mining capabilities. At a higher conceptual
level, the creation of an intelligent virtual environment from point
clouds can be inspired by our cognitive system: recognizing an
object means accessing symbolic units stored in a semantic
memory and which are abstract from our previous experiences

while being independent from any context. Disposing of either
digital copies of the real world, invention / conception of “things”
to be integrated in the world or a combination of both, we refer
to geometries from the “physical space” and “fictional space”
(immaterial, concept-based) as in (Billen et al., 2012). In their
paper, they propose an ontology of space in order to facilitate an
explicit definition of CityGML. By extending the formalism, it
constitutes a basis for semantic injection into point clouds.
However, the study of ethno physiography as well as human
cognition of geospatial information is mandatory for defining
information system ontologies. Indeed, the closer (and the richer)
the model is to the domain concept, the better (and more
extensible) the ontology will be. But the questions of how
detailed an ontology should be dependent on the levels of
interoperability that is envisioned.
3.1 Conceptual SPC (Smart Point Cloud) Model
The purpose of the SPC characterization is to represent the real
world spatially described by point clouds in a computerized form:
a user-centered frame representation serving an intelligent
environment. The definition of a generic model that applies to a
general purpose is very complex, as opening on all domains that
benefit from 3D semantically rich models and point clouds range
from neuro-psychiatry to economics or geo-information. Our
approach was thought to allow a maximum flexibility by defining
a conceptualization on which different domain formalization
can be attached (Figure 1).
Figure 1 Meta-model articulation for the creation of a SPC
It is for this reason that we wanted to clearly illustrate a privileged
domain of application: indoor environments (for BIM,
emergency response, inventory management, UAV collision
detection …). Therefore, we divided the characterization
(knowledge representation and data modelling) in different
hierarchical levels of abstraction to (1) avoid overlap to existing
models, and (2) enhance the flexibility and opening to all possible
formalized structure. The core instruction is that the lower levels
are closer to a domain representation than higher levels (level-0
being the highest level) but they impose their constraints. The
overall structure can be seen as a pyramidal assembly, allowing
the resolution of thematic problems at lower levels with reference
to constraints formally imposed by the higher levels.
Knowledge integration is essential to the creation of the SPC
structure, as it constitutes the necessary source for the meaning
and adaptation of different entities within the pyramidal model.
By default, we integrate a core external algorithmic module that
allows to extract a raw relationship graph based on a voxel-based
element mining routine inspired by (Gorte and Pfeifer, 2004;
Wang et al., 2017). This was established as it does not require
any external semantic information other than pure spatial
information which encourage flexibility and adaptation.
However, more domain-verse classification modules such as
(Xiong et al., 2013) provide potentially enhanced workflows. As
seen beforehand, while RDBMS are a great fit logically speaking,
they do not perform well considering the very high number of
rows. Clustering via indexing-schemes is mandatory for
interactive visualisation as well as efficient data loading, inserts
and updates. Building this spatial structure over an object-based
binary host/guest structure would provide powerful analysis and
visualization exploitation. In parallel, an ontology structure
allows inference reasoning and semantics retention, and is
directly linked to the spatial structure thus defining relationships
and topology for points and objects.
The top level, called level-0 gathers data, information, and
knowledge about the core SPC components.
3.2 Level-0: Generalized SPC meta-model
For clarity, we specifically target point clouds, but the model can
easily be extended to all kind of gathered data from our physical
world, and in an extended version provide an opening for 3D
Figure 2 Level-0 Generalized SPC meta-model (UML). A point cloud constituted of points is block-wise organized through
semantic patches. These can be pure spatial conglomerate or retain a coherent semantic relationship between constituting points.
Generalizations via different schemes are possible using the generalisation structure to provide additional analysis flexibility.

meshes or parametric model integration. The different meta-
models are formalised in UML, and provide a conceptual
definition for implementations. We therefore modelled as a goal
to provide a clear vision and comprehension of the underlying
system, but the database creation slightly differs to privilege
performances, therefore adaptations are made at the relation
scheme modelling level.
The generalized SPC meta-model (Figure 2) formalizes the core
components needed for constituting semantic point patches. It
starts with the most primitive geometry: a point. It has a position
defined by three coordinates in Euclidean space (R3
): X, Y and
Z. Each point has a limited number of attributes, for an example
in Figure 2 derived from 3 different sources: device knowledge
(scan angle, intensity …), analytic knowledge (normal,
curvature, roughness …) or domain knowledge (definition,
representativeness …). While the UML model shows a one to
many relationship, to avoid too many SQL joints and for
performances sake, the attributes can directly be integrated
within the point table (the same applies to semanticPatch).
However, it is important to note that one point can have many
sets of attributes (consequently, as does a semanticPatch).
Figure 3 Example of a basic LOD n-1 Generalization of 3
SemanticPatches from a point cloud with color attributes only.
A collection of points sharing the same type of dimensions
(spatial and semantic) constitute a point cloud. This is a data-
driven aggregation, as depending on the definition of the dataset,
the point cloud object parameters will differ. However, one
dataset often represents a coherent point aggregation which
serves a domain purpose. This point cloud entity also benefits
from a knowledge source identifier to identify which knowledge
source it relates to (if multiple domain-specific ontologies are
connected to the model). To cope with heterogeneity in point
cloud sources, a schema is defined and attached to all point
clouds that share a similar dimension number, dimension type,
scale and offset, similarly to (Cura et al., 2017). Each point
cloud is then parsed in semantic patches, regarding available
knowledge and an adapted subdivision technique. Arbitrary,
such a technique could be point-number related, geometry
related or position related. While the existing postgreSQL
plugin pgpointcloud defining patches in a XML scheme
provides spatial patches, we propose to greatly enhance such an
approach by constructing semantic patches, which retain both
spatial and semantic properties. It constitutes small spatial
subsets of points that share a relationship based on available (and
injected) knowledge. By default our proposed voxel-based
subdivision method groups point using geometrical &
topological properties that implicitly relate to abstract
conceptualization of our mind (such as geometric shapes to group
points belonging to a plane, others floating above it …). As such,
they are indirectly semantized. “semanticPatch” retains many
attributes, with an emphasis on two specifics: a classification
status (which can be 0:unclassified, 1:one class only or 2:many
classes), and a confidence level for the classification. These are
computed through a segmentation and classification routine as
described in 4, which is independently developed from the
proposed point cloud data model. In order to speed up
computations, allow enhanced spatial & semantic searches and
provide new generalization possibilities to better address our
representation of the data, a LoD generalization structure
definition is directly linked to the semantic patches (Figure 3). It
defines the indexation scheme used, the different levels (if any),
its node spatial extent and neighbours, associated geometries (if
any) and other generalized attributes derived from statistical
computations (average, Gaussian mixture …). (Poux et al.,
2016a) suggests a 3DOR-Tree as defined in (Gong and Ke, 2011)
for improved performances, but hashing and implicit storage
(Cura et al., 2016) can also greatly improve the internal
coherence.
3.3 Level-1: Connection-layer meta-model
The connection-layer meta-model (i.e. the strict framework that
drives the use of a formalism and resolves any ambiguities about
the use of its concepts) plays the role of a plug system: an
interface between the core SPC level-0 generalized meta-model,
and a domain ontology that formalizes the domain-specialization
of a generic ontology. It is constituted of two sub-levels, L1-1
and L1-2 (Figure 4).
The core element in this meta-model is created by an aggregation
of one or many patches that define a connected element
(connectedElements). These are the entities that closely relate to
classified objects, retaining both spatial and metadata coherence.
Connected elements transparently describe a portion of the space
that is by default indirectly influenced by analytical knowledge
and device knowledge, from the underlying patch organization.
Connected elements have a spatial extent computed from the
aggregation of patches, as well as one or several geometries that
can be obtained by topological calculations from the patches.
Aside from geometrical attributes including a spatial
generalization (which can be for example the barycentre of the
spatial extent, but also more representative statistical
generalization) they retain raw semantics from the underlying
patch aggregation rule. This can be ConnectedElement,
VerticalElement or HorizontalElement, but the integration of
domain knowledge gives the opportunity at this level to deepen
the representativeness of a connected element. Nevertheless, one
connected element regarding a variety of applications can have
different spatio-semantic interests. Therefore, aggregated
elements constitute an aggregation of connected elements which
provide additional granularity and flexibility (a chair, with 4 feet
and one sitting area, which is either 5 connected elements or one
aggregate element). In a same philosophy, each connected
element retains basic relationships with its surrounding
environment. We therefore detect and store host and guest
relationship information (the table is the guest of the floor, and
the floor is the host of the table). These strong concepts have an
influence on how deep the selectivity can go. Retaining relations
and organizing hierarchically through topological relations refers
to mereology, applied on point clouds object generalization
regarding DE-9IM (Clementini and Di Felice, 1997). Therefore,
a double structural definition retaining generalization and point
primitives (Level-0) allows new analysis combining multi-LoD
definitions. This pyramidal graph relationship formalization
permits to easily access a spatially connected graph for reasoning
engines that interpret topological relations. These conditions can
be used to infer a physical description and combine many
possible analysis, for example the possibility to recreate
occluded zones, reason about position in time and space and
conduct structural investigation.

Connected elements also have additional properties and specific
attributes inherited from the patches that it relates to. While
every point can retain a date stamp, a connected element can be
influenced by temporal variations, but duplicating a physical
description of the connected element at every discrete temporal
interval would not be sufficient. Therefore, connectedElements
can have a temporal modifier that will describe the different
modifications from the in-base initial state. They can also relate
to multiple spaces, which define a set of dimensions in R, R2
…
Rn
(for example X/Y/Z in R3
).
“Space” and “ConnectedElements” are connected to a lower
abstraction level L1-2 within the connection-layer. A space can
have many subspaces defined in respect to the space dimensions.
In a spatial context, it is interesting to note that they are mostly
fiat subspaces in regard to (Smith and Varzi, 2000). Indeed, bona
fide boundaries represent physical separators whereas fiat
boundaries will describe a fictional border, and most of subspaces
for human cognition have a fictional border (i.e. a room with an
open door). The topological inward relation allows to constitute
different subspace LoDs (we can consider a building, or the first
floor of that building, or the room 2/43 of that first floor …).
“subSpace” therefore retain a domain knowledge source pointer
that can be dedicated to one or many specific domains (it can be
a subspace in regard to the ontology of buildings, to the
archaeology temporal findings in Australia …). The concept of
world objects results from the definition of (Billen et al., 2012),
which is a mind conceptualization of an object that also follows
the categorization of (Smith and Varzi, 2000). “worldObject” is
a specialisation of “connectedElements” retaining a domain
related semantic pointer similarly to “subSpace” (a knowledge
source mirroring the domain conceptualization). Geometries
attached to these entities are useful for topological calculations
and the direct link to “subSpace” allows many possible queries
for information extraction (testing the inclusion of a world object
in a subspace, testing the intersection of two objects geometries
with a fiat boundary from a subspace …). “subSpace” and
“worldObject” constitute the entry points on which domain
ontologies can plug themselves to adapt to a specific application.
3.4 Level-2: Domain adaptation
As stated by (Tangelder and Veltkamp, 2007) “any fully-fledged
system should apply as much domain knowledge as possible, in
order to make shape retrieval effective”. With the rise of online
solutions, we have seen a great potential in using knowledge
database for classification to analogically associate shapes and
groups of points with similar features. This association through
analogy “is carried out by rational thinking and focuses on
structural/functional similarities between two things and hence
their differences. Thus, analogy helps us understand the unknown
through the known and bridge gap between an image and a
logical model” (Nonaka et al., 1996). This introduces the concept
of data association for data mining, and relationships between
seemingly unrelated data in a relational database or other
information repositories. Enabling the use and analysis of domain
knowledge through explicit domain assumptions while
separating domain knowledge from operational knowledge refers
to domain ontologies. This shares interoperability notions with
our proposed SPC structure; while one domain meta-model
formalization is suited for some applications, another can be
more adapted for others and create different results that will be
used differently. These will dictates how the final point cloud
data model should be used (for which application).
Figure 4 Level-1 Connection-layer meta-model. It is directly linked to the Level-0: semantic patches constitute connectedElements.
aggregatedElements and topological notions gives flexibility to the deepness of an element characterization. ConnectedElements can
relate to one or multiple spaces defined by their dimensions. These are subsequently divided in subspaces regarding a concept from a
domain knowledge characterization, similarly to the world objects (being a specialization of connectedElements).

Therefore, the level-2 meta-model is directly linked to different
knowledge sources, which are specified in the level-1(-2) meta-
model interfaces: “subSpace” and “worldObject”. Their
conceptual abstraction in between pure spatial data (point clouds)
and specific domain-verse data constitute a generic door for the
potential connection to many level-2 domain specialization. This
allows a great flexibility and a context adaptation to a very wide
range of application, limited only by the underlying domain
ontology. In fine, the domain meta-model attached to the
connection-layer meta-model, and indirectly to the generalization
meta-model constitute the SPC model.
In a simple example (Figure 5), we illustrate over a basic indoor
ontology the connection of a level-2 meta-model to the
connection-layer meta-model. It contains 2 class elements
(separatorElement and internalElement) specialized in 8 classes
(transitionSeparator, verticalSeparator, horizontalSeparator,
livingElement, mepElement, madeMadeStructElement,
moveableElement, noise) which can also be specialized in a
refinement process to get as close possible from the abstract idea
that the human mind has of a concrete or abstract object of
thought. This crude example is inspired by already established
BIM standards and is used on a simple test case to enable rapid
perception of natural language requests. As such, one selected
“worldObject” can be specialized and identified as an
internalElement, a mepElement (Mechanical, Engineering,
Plumbing), specifically a duct of the “subspace” room 4 in the
higher LoD level “subspace” building 7, and attached by an
“externalFixture” next to the exit “door”. The possibility to play
on all possible scales is therefore an opening on a flexible system
that can be adapted to many real-world applications.
4. PROTOTYPE & DISCUSSIONS
Any modeling choice is arbitrary and depends on the conscious
or unconscious aspirations of the designer. Although our work
responds to a concern for generalization at a spatio-semantic
level, it nevertheless remains that it is not totally independent of
a certain context. It is for this reason that we wanted to clearly
illustrate a privileged domain of application, indoor
environments (for BIM, emergency response, inventory
management, UAV collision detection …). This choice permits
to explore different scales and configurations for deeply and
entirely testing our developments. It is also ideal for the
definition of new virtual spaces, and the GIS demand associated
to such environment is ever increasing. Therefore, as the
formalization of domain constantly evolve, modelisation and
direct integrations of level-2 domain meta-models will be
explored in future research.
A first prototype is implemented that addresses level-0 and level-
1 SPC conceptual model. The implementation was developed
under Linux (Ubuntu), using several python, C++ and SQL
libraries including psycopg, pdal, gdal, pcl, CCLib,
pgpointcloud, postGIS. We integrated the results in the open
source DBMS PostgreSQL. Different informations (spatial and
radiometric) were fused regarding (Poux et al., 2016b), and a
multi-scale voxel structure is computed to enable recognition of
independent spatial entities by voxel adjacency-study. While the
SPC model at Level-0 doesn’t necessitate any semantics to
function (spatial attributes {X, Y, Z} only), their pertinence to
domain applications greatly orient the creation of patches.
Therefore, point grouping rules influence the performances of
information extractions. While this is done spatially in the
prototype via a voxel-based topological analysis as in (Poux et
al., 2017), other criteria will be explored. Each detected element
is parsed in semantic patches arbitrary subdivided based on a
point maximum number of 800 points and directly integrated in
PostgreSQL. The database is populated and we obtain semantic
patches that constitute connectedElements, retaining both spatial
and semantic information. Using PostGIS and SQL statements
(i.e Table 1), information extraction is possible.
Abs.
level
Goal SQL Statement
0 I want to select the
‘semanticpatches’
which intersects a
defined polygon
SELECT pa FROM
semanticpatch WHERE
ST_INTERSECT(pa::geo
metry) = TRUE
0 I want to select all
‘semanticpatches’ that
have been classified
SELECT pa FROM
semanticpatch WHERE
spclassifstatus = 1
1 I want to select the
connected element
CC0065
SELECT pa FROM
semanticpatch WHERE
connectedelement_id = 65
Table 1 Basic SQL statements for level-0/1 abstraction
The integration of semantics to create a more intelligent structure
such as “The connected element CC0065 is a chair named
‘rocking_chair’ made in 1962 for relaxing” as SQL statements
(INSERT INTO moveableelement connectedelement_id, type,
title, date_prod, kind) VALUES ('65', ‘chair’, 'rocking_chair',
'1990-07-13', 'relaxing');) is also taking advantage of the SPC
structuration. Then, dual spatio-semantic queries leveraging the
linked domain concepts from a level-2 meta-model mirror our
real world information gathering (i.e. natural language query: “I
want to locate all cypress in the garden, calculate the min.
Figure 5 Level-2 meta-model example. separatorElement and internalElement are connected to the Level-1 meta-model
directly through “worldObject” and “subSpace”. It is a succession of specialization describing an indoor environment.

distance to surrounding buildings and if the distance to buildings
is inferior to their height, then I want to calculate how much
length I can cut if they are not protected” illustrated in Figure 6).
Figure 6 Example of a point cloud spatio-semantic query
While 3-dimensional spaces are strongly inferred in the SPC
model, 4-dimension spaces integrating time or by extension n-
dimensional spaces are possible characterizations for greater
interoperability. Tests were conducted with static point data only,
but varying positions in space and time present additional
problematics that will be investigated. While our present paper is
a proof of concept that provide direct integration of hard-coded
or computed domain knowledge, our next work will include the
extensibility of the proposed model to other data types, as well as
a better integration of learning routines and ontologies as
knowledge sources. A direct workflow to bring intelligence in
real-time is currently investigated.
5. CONCLUSION
Through the definition of articulated meta-models, we propose a
new data model giving point clouds the possibility to retain
semantic concepts. Considering the variability in their definition
due to heterogeneous knowledge sources, we proposed an all-
round solution to semantic injection and spatio-semantic queries.
Its conception through different conceptual levels allows to better
integrate point clouds in existing workflows, retaining a high
interoperability potential with existing and future standards.
While our proposed approach is illustrated on point cloud data, it
can be extended to all kind of complex data that represent
physical components. A special focus was laid on the dual
aggregation hierarchies of semantic feature types and geometric
decompositions. Future work will include the integration of the
multi-level meta-model with point cloud processing frameworks
for knowledge discovery.
6. ACKNOWLEDGMENT
The authors would like to thank the anonymous reviewers for
reviewing this manuscript. We also thank Valentin Blind for his
valuable feedback.
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