# A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Building a Knowledge Base for Automatic Feature Recognition

- (1)
- identification of motivating scenarios and the scope of ontology;
- (2)
- definition of competency questions;
- (3)
- building the ontology (ontology capture, ontology coding and integrating the existing ones);
- (4)
- validation of the ontology according to the requirements set by competency question;
- (5)
- maintenance of ontology after verification.

#### 3.1. Conceptual Framework for Automatic 3D Modeling and Feature Recognition from Point Clouds of Urban Scenes

#### 3.2. Definition of Concepts

#### 3.3. Modularity of Concept in an Urban Scene

#### 3.3.1. Elevation Perspective

#### 3.3.2. Functionality Perspective

#### 3.3.3. Nature of Objects Perspective

#### 3.3.4. Geometry Module

#### 3.3.5. Composition Module

- Components aggregation: an individual object can be broken down into some components that cannot be decomposed into any small parts. For example, in geometric model of a building, the patch representing a wall may not be divided into smaller pieces.
- Subsystem aggregation: this relationship indicates the abstract concepts for representing functionally relevant sets. For example, a parking lot area comprises of a piece of ground with some vehicles, some sign poles and some poles for paying the parking fee.
- System aggregation: this level is used to represent the top-level aggregation relationships among objects in an independent scene or objects in a network. Examples include transportation system containing many parts severing for transportation.

#### 3.3.6. Spatial Relations Module

- If the planes are parallel, these two planar regions are disjoint.
- If the planes are coplanar, the relation between two planar regions is determined as in 2D space.
- If the planes are intersecting, two planar regions can have many possible topological relations.

- ${A}^{\xb0}$ = indicates the interior of the region A;
- $\partial A$ = the boundary of the region A;
- ${B}^{\xb0}$ = the interior of the region B;
- $\partial B$ = the boundary of the region B;
- $Il$ = intersection of two planes containing planar regions A and B;
- $\zeta $ records the topological relations of the primitives comprised of the common parts between the planar region A and B and intersection line. The primitives are all located on the intersection line;
- dim() = dimension operator.

- (1)
- The relation between the planar region A and the intersection line Il, including Disjoint, Meet and Overlap;
- (2)
- The relation between the planar region B and the intersection line Il;
- (3)
- The relations between primitives on the intersection line IL that are the common part comprised of planar region A and the intersection line and the common part comprised of planar region B and the intersection line.

#### 3.4. Objects Attributes

#### 3.5. Constraints

- Geometric dimensional constraints: for feature recognition, the essential and intrinsic attributes of objects, including measurable attributes, geometry shape attributes, limit the rough classification of objects.
- Spatial relations constraints: spatial constraints link objects in a local part of the urban scene. For some objects belonging to the transportation system, cars are moving on the road surface. Sidewalks are extending following the road or connected to roads. Traffic sign pole or light pole located near to the roads or sidewalks. Especially for man-made objects, components of objects have some topological relations constraints in the aspect of design or functional requirements. These constraints also can be represented as rules in the knowledge base.
- Logical constraints: some constraints are given not for the measurable or spatial constraints but from the view of logic. An example for interpreting logical constraints is that a parking lot is a piece of ground where accommodates a large amount of orderly arranged vehicles. Because logical constraints could associate concepts according to their logical relations of functions, locations, and system relevance, they are defined in the level of relevance among components of objects. Similarly, they can be defined in the level of subsystem consisting of objects.

#### 3.6. Relationships Definition

- Hyponymy: it is the “is-a” relationship. It is the semantic relation of being subordinate or belonging to a lower rank or class [42]. Relationships including the definition of the kinds of concept constitute the backbone of ontological taxonomy tree structure. “is-a” relationship also contains some converted relationships, including synonymy and antonymy relations. “isEquivalentTo” and “isSimiliarTo” belong to synonymy relations. At the same time, “isDisjoint” and “isOpposite” are main relationships of antonymy [57].
- Meronymy: it is the “whole-part” relationship. It indicates the relationship of grouping concepts as a whole or decomposing concepts into parts. The relationships of “isPartof” and “isComposedof” are commonly defined in whole-part relations among concepts. In OWL ontologies, there are listed use cases of whole-part relations, such as defining “whole-part” relationships for individuals and class definition. Although the relationship “subclassOf” and “kind of” all are used to organize concepts hierarchically, their distinction must be made to decide the relationship in hierarchical concepts [58], including descriptive relations, possessive attributes (“has” relation), spatial relationship (locateAt, connect, align, parallel, vertical, direction, above, on, in), function relationship (hasFunction), and composition relations (must-beComposedOf, could-beComposedOf).

#### 3.7. Axioms

Plane(?P1), Plane(?P2), Line(?L1), isPerpendicularTo(?P1,?L1), isPerpendicularTo(?P2,?L1) -> isParallel(?P1,?P2) |

Line(?L1), Line(?L2), Line(?L3), isPerpendicularTo(?L1,?L3), isPerpendicularTo(?L2,?L3) -> isParellel(?L1,?L2) |

Plane(?P1), Plane(?P2), Plane(?P3), Plane(?P4), isPerpendicularTo(?P1,?P3), isPerpendicularTo(?P1,?P4), isPerpendicularTo (?P3,?P4), isPerpendicularTo(?P2,?P3), isPerpendicularTo (?P2,?P4) -> isParallel(P1,?P2) |

## 4. Experimentation and Results

#### 4.1. Consistency Check in Protégé

#### 4.2. Reasoning Experiments Based on Knowledge Base

#### 4.2.1. Experiment of Recognizing a Cuboid from Planar Regions

PlanarRegion(?Pr1), PlanarRegion(?Pr2), isNeighboringTo(?Pr1,?Pr2), isMeet_Meet_Meet_Equal(?Pr1,?Pr2), isVerticalTo(?Pr1,?Pr2) -> isMeet_Equal_Vertical(?Pr1,?Pr2) | (1) |

PlanarRegion(?Pr1), PlanarRegion(?Pr2), PlanarRegion(?Pr3), PlanarRegion(?Pr4), PlanarRegion(?Pr6), Rectangle(?Pr1), isNeighboringTo(?Pr2,?Pr1), isNeighboringTo(?Pr2,?Pr3), isNeighboringTo(?Pr2,?Pr4), isNeighboringTo(?Pr2,?Pr5), isMeet_Equal_Vertical(?Pr2,?Pr1), isMeet_Equal_Vertical(?Pr2,?Pr3), isMeet_Equal_Vertical(?Pr2,?Pr4), isMeet_Equal_Vertical(?Pr2,?Pr6) -> FacetofCuboid(?Pr2) | (2) |

Set(?A), PlanarRegion(?Pr1), PlanarRegion(?Pr2), PlanarRegion(?Pr3), PlanarRegion(?Pr4), PlanarRegion(?Pr5), PlanarRegion(?Pr6), isInSet(?Pr1,?A), isInSet(?Pr2,?A), isInSet(?Pr3,?A), isInSet(?Pr4,?A), isInSet(?Pr5,?A), isInSet(?Pr6,?A), FacetofCuboid(?Pr1), FacetofCuboid(?Pr2), FacetofCuboid(?Pr3), FacetofCuboid(?Pr4), FacetofCuboid(?Pr5), FacetofCuboid(?Pr6) -> Cuboid(?A) | (3) |

#### 4.2.2. Axioms and Rules to Formally Define a Hip Roof from Planar Regions

Set(?B), Wall(?W1), Wall(?W2), Wall(?W3), Wall(?W4), Trapezoid(?Trap), Triangle(?Tri), PlanarRegion(?Pra1), isInSet(?Pra1,?B), ComponentsofRoof(?Pra1), PlanarRegion(?Pra2), isInSet(?Pra2,?B), ComponentsofRoof(?Pra2), PlanarRegion(?Pra3), isInSet(?Pra3,?B), ComponentsofRoof(?Pra3), PlanarRegion(?Pra4), isInSet(?Pra4,?B), ComponentsofRoof(?Pra4), hasShape(?Pra1,?Tri), hasShape(?Pra2,?Trap), hasShape(?Pra3,?Tri), hasShape(?Pra4,?Trap), isMeet_Meet_Meet_Equal(?Pra1,?Pra4), isMeet_Meet_Meet_Equal(?Pra1,?Pra2), isMeet_Meet_Meet_Equal(?Pra3,?Pra4), isMeet_Meet_Meet_Equal(?Pra3,?Pra2), isMeet_Meet_Meet_Equal(?Pra2,?Pra3), isMeet_Meet_Meet_Equal(?Pra2,?Pra1), isMeet_Meet_Meet_Equal(?Pra2,?Pra4), isMeet_Meet_Meet_Equal(?Pra4,?Pra3), isMeet_Meet_Meet_Equal(?Pra4,?Pra1), isMeet_Meet_Meet_Equal(?Pra4,?Pra2), isSlopeTo(?Pra1,?W1), isSlopeTo(?Pra2,?W2), isSlopeTo(?Pra3,?W3), isSlopeTo(?Pra4,?W4) -> HipRoof(?B) | (4) |

#### 4.2.3. Experiment for Recognizing a Hip Roof from Point Clouds

Set(?B), Ground(?g), Trapezoid(?Trap), Triangle(?Tri), PlanarRegion(?Pra1), isInSet(?Pra1,?B), ComponentsofRoof(?Pra1), PlanarRegion(?Pra2), isInSet(?Pra2,?B), ComponentsofRoof(?Pra2), PlanarRegion(?Pra3), isInSet(?Pra3,?B), ComponentsofRoof(?Pra3), PlanarRegion(?Pra4), isInSet(?Pra4,?B), ComponentsofRoof(?Pra4), hasShape(?Pra1,?Tri), hasShape(?Pra2,?Trap), hasShape(?Pra3,?Tri), hasShape(?Pra4,?Trap), isMeet_Meet_Meet_Equal(?Pra1,?Pra4), isMeet_Meet_Meet_Equal(?Pra1,?Pra2), isMeet_Meet_Meet_Equal(?Pra3,?Pra4), isMeet_Meet_Meet_Equal(?Pra3,?Pra2), isMeet_Meet_Meet_Equal(?Pra2,?Pra3), isMeet_Meet_Meet_Equal(?Pra2,?Pra1), isMeet_Meet_Meet_Equal(?Pra2,?Pra4), isMeet_Meet_Meet_Equal(?Pra4,?Pra3), isMeet_Meet_Meet_Equal(?Pra4,?Pra1), isMeet_Meet_Meet_Equal(?Pra4,?Pra2), isSlopeTo(?Pra1,?g), isSlopeTo(?Pra2,?g), isSlopeTo(?Pra3,?g), isSlopeTo(?Pra4,?g) -> HipRoof(?B) | (5) |

#### 4.2.4. Experiment for Recognizing Semantic Features of Buildings from Point Clouds

## 5. Conclusions and Future Work

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Proposed conceptual framework for automatic 3D modeling and feature recognition from point clouds.

**Figure 6.**Classification of topological relations in 3D space (

**A**), and their formalized representation in the ontology (

**B**).

**Figure 7.**Illustration of an example of a topological relation between two planar regions (A and B) in 3D space.

**Figure 11.**A building model with hip roof structure (the numbers 1 to 4 represent the individuals Pra1, Pra2, Pra3 and Pra4. W1 and W2 are the individuals of class “Wall”).

**Figure 13.**(

**A**) Segmentation results; (

**B**) boundaries and (

**C**–

**E**) the process of the determination of topological relations among components extracted from point cloud.

**Figure 14.**Impacts of missing data on feature recognition. (

**A**) The missing part located in the interior of planar segment has no impacts; (

**B**) The missing parts of the interior and boundary do not impact feature recognition; (

**C**) The missing parts of the interior and boundary impacts the identification of topological relations; (

**D**) A large area of missing part has impacts on the determination of geometric shapes and topological relations.

**Figure 15.**Recognition of semantic features of the components of a building from point cloud. (

**A**) A cluster; (

**B**) planar segments after region growing processing; (

**C**) planes detected by RANSAC algorithm using plane models; (

**D**) a potential roof structure; (

**E**) a potential wall structure.

Information Type | Terms | Examples |
---|---|---|

Quantitative Elements | Geometric Dimension | length, width, height, radius, thickness, area, volume |

Geographic Coordinate | latitude, longitude, elevation | |

Local Coordinates | X, Y, Z | |

Properties of Point Clouds | intensity, return number, point source ID, classification, color | |

Qualitative Elements | Object Types | building, car, road, tree, pole, etc. |

Geometric Shape | circle, rectangle, ellipsoidal, cross-sectional shape, line, cylinder, cuboid | |

Surface Type | plane, curved surface | |

Dependence | logical dependence, geographic dependence, physical dependence | |

Topology | 2D and 3D topology | |

Function Relevance | interrelated relation for functions | |

Surrounding Attributes | the neighboring information and their relations | |

Architecture Components | wall, roof, floor, door, windows, balcony, etc. | |

Roof Shapes | flat, shed, gable, hip, barrel, etc. | |

Material Attributes | concrete, wood, asphalt | |

Geometric Relations | parallel, perpendicular, intersecting, coplanar, etc. |

**Table 2.**Basic topological relations between primitives on the intersection line [56].

Type of Relations | Graphical Representation | Topological Relations |
---|---|---|

Point-point relations | Disjoint, Equal | |

Line segment-point relations | Disjoint, Meet, Contain | |

Line segment-line segment relations | Disjoint, Meet, Overlap, Cover, Contain, Equal |

Attribute Types | Explanation | Examples |
---|---|---|

Dimensional attributes | measurable quantitative dimension of objects | size, height, length, width, area |

Geometric shape attributes | describe geometric shapes | normal, boundary, surface type (plane, curved), shape(rectangle, square, circle) |

Spatial attributes | location-related attributes and spatial relations | X-coordinate, Y-coordinate, Z-coordinate, latitude, longitude |

Function attributes | object functions in a system or the roles of objects in a scene | lighting (for light pole), control traffic (for traffic sign), passing (for door) |

Dependency attributes | attributes representing the interdependency between components or objects | logical dependency, geographic dependency, location dependency |

System (combination) attributes | attributes are the terms for a group of objects or a subsystem | roof styles (such as gable, hip, shed, flat, and mansard and so on), traffic system, intersection |

Semantic Features | Rules | Explanation | Rule ID |
---|---|---|---|

Wall | PlanarRegion(?pr_i), isVerticalTo(?pr_i,?ground), Ground(?ground), hasDirection(?ground,(0,0,1)), hasArea(?pr_i,?area_i), greaterThan(?area_i,2) -> Wall(?pr_i) | A wall is a plane that is vertical to ground and its area it greater than 2 m^{2} | (6) |

PlanarRegion(?pr_j), isCoplanarTo(?pr_j,?plane_i), Wall(?pr_j) -> Wall(?pr_j) | If a plane is coplanar to a wall, it is wall | (7) | |

PlanarRegion(?pr_k), isConnectTo(?pr_k,?pr_i), Wall(?pr_i), isVerticalTo(?pr_k,?ground), Ground(?ground), hasDirection(?ground,(0,0,1)) -> Wall(?pr_k) | If a plane connects to a wall and is vertical to ground, it is wall | (8) | |

PlanarRegion(?pr_j), Wall(?pr_i), isConnectTo(?pr_j,?pr_i), isCoplanarTo(?pr_j,?pr_i), -> isSameWall(?pr_j,?pr_i) | If a plane connects to a wall and is coplanar to this wall, they belong to same wall | (9) | |

Roof | PlanarRegion(?pr_i), hasArea(?pr_i,?area_i), greaterThan(?area_i,2), isSlopeTo(?pr_i,?ground), Ground(?ground), hasDirection(?ground,(0,0,1)), hasSlopeAngle(?pr_i,?ang_i), lessThan(?ang _i,70), hasHeightAttribute(?pr_i,?upperMost) -> ComponentsofRoof(?pr_i) | A roof component has covering function on the uppermost part of a building | (10) |

PlanarRegion(?pr_i), ComponentsofRoof(?pr_j), isSlopeTo(?pr_i,?ground), Ground(?ground), hasDirection(?ground,(0,0,1)), isConnectTo(?pr_i,?pr_j), hasSlopeAngle(?pr_i,?ang_i), lessThan(?ang _i,70), hasHeightAttribute(?pr_i,?upperMost) -> ComponentsofRoof(?pr_i) | (11) | ||

Gable roof style | Set(?B), isInSet(?pr1,?B), isInSet(?pr2,?B), ComponentsofRoof(?pr1), ComponentsofRoof(?pr2), isMeet_Meet_Meet(?pr1,?pr2), Line(?line1) -> hasIntersectLine(?B,?line) | A gable roof consists of two roof sections sloping in opposite directions and the highest, horizontal edges meet to form the roof ridge. (v_g = (0,0,1)) | (12) |

Set(?B), isInSet(?pr1,?B), isInSet(?pr2,?B), ComponentsofRoof(?pr1), ComponentsofRoof(?pr2), hasDirection(?pr1,?v1), isLeftSide(?v1,?v_g), hasDirection(?pr2,?v2), isRightSide(?v2,?v_g), Line(?line1), isParallelTo(?line1,?ground), Ground(?ground), hasDirection(?ground,?v_g), higherThan(?line1,?pr1), higherThan(?line1,?pr2) -> GableRoof(?B) | (13) |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Xing, X.-F.; Mostafavi, M.-A.; Chavoshi, S.H.
A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene. *ISPRS Int. J. Geo-Inf.* **2018**, *7*, 28.
https://doi.org/10.3390/ijgi7010028

**AMA Style**

Xing X-F, Mostafavi M-A, Chavoshi SH.
A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene. *ISPRS International Journal of Geo-Information*. 2018; 7(1):28.
https://doi.org/10.3390/ijgi7010028

**Chicago/Turabian Style**

Xing, Xu-Feng, Mir-Abolfazl Mostafavi, and Seyed Hossein Chavoshi.
2018. "A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene" *ISPRS International Journal of Geo-Information* 7, no. 1: 28.
https://doi.org/10.3390/ijgi7010028