When you need accurate floor plans, reliable measurements, or a clear understanding of how a building is laid out, indoor mapping becomes essential.
Professionals working with existing buildings - surveyors, architects, real estate teams, facility managers - all face the same challenge: they need spatial information they can trust, without spending days on site or relying on outdated drawings. Manual measurements and fragmented documentation often lead to uncertainty, rework, and lost time.
Indoor mapping addresses this by capturing interior spaces as scanned data and converting them into structured digital outputs. Buildings are documented as they actually exist, providing a reliable basis for measurement, analysis, and decision-making.
Indoor mapping is widely used, but rarely explained end to end. Many resources focus on scanning hardware, software features, or final floor plans - without connecting the full workflow.
This guide brings everything together.
For those who want to dive deeper, you can always download our free white paper for a complete overview of indoor mapping.
In this guide, you’ll learn:
You can read the guide from top to bottom or jump directly to the section most relevant to your work. Below, we start with the fundamentals.
Indoor mapping is the process of creating accurate digital representations of interior spaces based on scanned data. Instead of relying on manually interpreted measurements or simplified drawings, indoor mapping captures the actual geometry of a building as it exists and converts it into structured, measurable information.
Indoor mapping is not intended to create visual models alone. Its primary goal is to deliver reliable spatial information that supports documentation, analysis, and decision-making across a wide range of professional use cases.
All geometry is derived directly from scanned data, ensuring that dimensions and layouts reflect real-world conditions rather than approximations.
Indoor mapping equipment is available in many forms, ranging from simple, accessible tools to highly accurate professional systems. The right choice depends on criteria like precision, ease of use, project size and scope.
€1,000–€2,500
iPhone and iPad devices can be used for basic indoor scans, quick measurements, and small residential projects, with limited accuracy and range.
€7,500–€40,000
Handheld or backpack systems that allow fast walk-through scanning of buildings, offering a strong balance between speed and centimetre-level accuracy.
€40,000–€80,000+
Advanced SLAM solutions with improved sensor fusion and drift correction, suited for large or complex buildings and professional-grade deliverables.
€50,000–€100,000+
Tripod-based scanners delivering the highest accuracy and point density, typically used for surveying, engineering, and BIM reference models.
In practice, indoor mapping follows a structured workflow that transforms physical spaces into digital outputs.
The process starts with capturing the interior environment using a 3D scanning device. This scan records spatial geometry as raw data rather than predefined drawings or selective measurements.
The captured data is then converted into a point cloud, which represents the building as a collection of spatial points in three dimensions.
Once the point cloud exists, it is processed digitally to extract spatial geometry. For indoor mapping workflows, manual pre-cleaning of the point cloud is mostly not required. Geometry is derived directly from the scanned data, allowing floor plans and spatial outputs to be generated efficiently.
From this processed dataset, structured outputs such as 2D or 3D floor plans are created. These outputs can then be reviewed, shared or exported for further use, all while remaining linked to the original scanned dataset.
Indoor mapping provides reliable spatial documentation, but its results are influenced by both the scanning technology used and the conditions of the environment during capture.
These limitations do not invalidate indoor mapping results, but they do affect data completeness and quality. Understanding them is essential when interpreting outputs and using indoor mapping data for measurement, analysis, or decision-making.
A building can be scanned whenever accurate and reusable spatial documentation is required. Scanning is not limited to a specific phase of a project; it is most valuable when a complete representation of a space is needed without relying on repeated site visits.
For indoor environments, scanning is commonly used when floor plans, layouts or measurements are required quickly and reliably. Because the entire interior is captured as a dataset, scanning is well suited for existing buildings, complex layouts, multi-level structures or situations where traditional drawings are missing or outdated.
More generally, scanning is applied when speed, completeness and accuracy are important, and when capturing the environment once - so it can be reused digitally later - offers a clear advantage over manual measurement approaches.
Scanning workflows are designed to significantly reduce the time between site capture and usable results. Data capture itself can be completed in a short time, after which all processing and output generation takes place digitally.
For indoor mapping, 2D and 3D floor plans can be generated within minutes rather than hours or days. This speed is achieved by eliminating manual drafting steps and avoiding unnecessary preparation such as manual point cloud pre-cleaning.
Because processing, sharing and collaboration are handled digitally, overall turnaround time is much shorter compared to workflows that rely on manual measurements or fragmented tools. This allows professionals to move rapidly from scanning to documentation.
The primary dataset produced for indoor mapping is a point cloud. A point cloud consists of a large number of spatial points, each representing a precise location in three-dimensional space.
This dataset captures interior surfaces such as floors, walls, ceilings and objects, preserving the geometric relationships between them. The point cloud itself is not the final deliverable, but it serves as the foundation for all further processing.
From this core dataset, structured outputs such as 2D and 3D floor plans, measurements and spatial representations are generated. Because all outputs originate from the same point cloud, consistency is maintained across different representations and use cases.
Scanning buildings means digitally capturing the physical geometry of a building or environment so it can be represented, analyzed and reused as data. Instead of working with isolated measurements or manually recorded dimensions, scanning records the entire space as it exists at the time of capture.
This approach applies to indoor environments as well as outdoor sites. The result is a complete spatial dataset that reflects real-world geometry rather than interpreted or estimated dimensions. Once captured, all measurements, plans and analyses are derived digitally from the same dataset.
By capturing the environment once and reusing the data multiple times, scanning shifts work from the field to digital processing and enables faster, more consistent documentation workflows.
Indoor scanning is mainly performed using two acquisition approaches: static scanning and mobile scanning. Each approach is associated with different scanning technologies and acquisition methods.
Both approaches produce point cloud datasets that can be processed for indoor mapping. The choice between static and mobile scanning influences acquisition speed, coverage, processing requirements, and achievable accuracy.
Static scanning is an indoor scanning approach where a 3D scanner captures geometry from fixed positions. During each scan, the scanner remains stationary and records surrounding elements such as walls, floors, ceilings, and structural features.
To cover an entire building, the scanner is repositioned to multiple locations. These individual scans are later aligned and combined into a single point cloud that represents the complete interior.
Static scanning is commonly known as terrestrial laser scanning (TLS). TLS systems are designed for stable data capture from fixed viewpoints and are widely used when high geometric accuracy is required.
Static scanning offers several advantages that make it suitable for detailed indoor documentation, but it also comes with practical limitations.
As a result, static scanning is best suited for situations where precision and detail are prioritized over speed.
Mobile scanning is a scanning approach where spatial data is captured while the scanner moves through an indoor environment. Instead of recording data from fixed positions, the scanner continuously captures geometry along a path as it is carried through the building.
A common form of mobile scanning used indoors is SLAM-based scanning. During a walkthrough of the building, the scanner records spatial data that is later reconstructed into a point cloud representing the interior space.
Because data is captured while moving, mobile scanning relies on processing steps after acquisition to reconstruct a coherent point cloud that represents the scanned environment.
Mobile scanning offers clear efficiency benefits for indoor environments, but these benefits come with technical and practical limitations that must be considered.
As a result, mobile scanning prioritizes speed and coverage, while data quality and accuracy depend strongly on processing and optimization after acquisition.
SLAM-based scanning and terrestrial laser scanning (TLS) are two commonly used approaches for indoor scanning. The key difference lies in how spatial data is captured and reconstructed.
Data is captured from fixed positions. The scanner remains stationary during acquisition. Prioritizes stability and high geometric accuracy. Requires multiple scan positions to cover a building. Acquisition becomes more time-intensive as building size increases.
Data is captured while moving through the environment. Geometry is recorded continuously along a trajectory. Prioritizes speed and efficient coverage. Large or complex interiors can be captured in a single walkthrough. Accuracy depends on movement, environment and post-processing.
| Difference | TLS (static scanning) | SLAM-based scanning |
|---|---|---|
| Accuracy | TLS generally provides higher absolute accuracy | SLAM provides sufficient accuracy for many documentation tasks but is more sensitive to drift |
| Speed | TLS requires more time on site | SLAM enables faster acquisition |
| Processing | — | SLAM workflows rely more strongly on processing and optimization after capture |
In practice, TLS is typically chosen when precision is the main priority, while SLAM-based scanning is chosen when speed and coverage are more important. Both approaches produce point cloud datasets that can be processed for indoor mapping.
The choice between static and mobile scanning depends on project priorities such as accuracy, speed, building size and operational constraints. Both approaches are widely used and are often complementary rather than mutually exclusive.
In practice, many projects combine both approaches. Mobile scanning is often used to capture large areas quickly, while static scanning is applied selectively in zones where higher precision is required. The optimal choice depends on balancing accuracy, efficiency and project constraints rather than on a single “best” method.
Indoor mapping requires 3D scanning hardware capable of capturing spatial geometry as point cloud data. While the hardware does not need to be proprietary to a specific software platform, it must be able to generate usable 3D scan data. Several types of scanners are commonly used for indoor mapping:
Stationary scanners placed at fixed positions. Capture highly accurate and detailed geometry. Require multiple scan positions to cover an entire building. Often used when precision is the primary requirement.
Handheld or wearable scanners that capture data while moving. Enable fast walkthrough-based scanning of interiors. Well suited for large or complex indoor environments. Rely on processing and optimization after capture.
Scanners mounted on carts or other mobile platforms. Used to increase stability and coverage in larger interiors. Operate on similar principles as mobile scanning systems.
Consumer devices equipped with integrated LiDAR. Can be used for indoor scanning within practical limits. Suitable for quick documentation and smaller spaces. Limited in range and accuracy compared to professional scanners.
Regardless of the scanner type, the essential requirement for indoor mapping is the ability to produce point cloud data that can be processed into structured indoor mapping outputs such as floor plans or spatial documentation.
The scanning approach directly influences accuracy. Static scanning benefits from stationary capture, which supports consistent and precise spatial measurements across scan positions.
Mobile scanning accuracy depends on movement, sensor capabilities and environmental conditions. Because data is captured along a trajectory, accuracy is influenced by how well the scanner’s position is maintained during the scan and how effectively the resulting data is processed.
Accuracy is not determined by capture alone but also by post-processing and optimization of the point cloud dataset.
A point cloud is a digital dataset that represents the geometry of a physical environment using a large number of individual points in three-dimensional space. Each point corresponds to a location where a surface was detected during scanning.
In the context of indoor mapping and building scanning, point clouds represent interior elements such as floors, walls, ceilings and structural features. Together, these points form a spatial representation of the scanned environment that reflects its real-world geometry.
A point cloud is not a drawing or model by itself. It is the raw spatial dataset from which further processing and structured outputs are derived. This makes it the foundational data layer for indoor mapping workflows.
A point cloud is important because it serves as the single source of spatial truth that underpins all further outputs and analyses.
Instead of relying on selectively measured dimensions or manually interpreted drawings, a point cloud captures the complete geometry of an environment at the time of scanning. This allows measurements, layouts and plans to be extracted digitally, based on the same underlying dataset.
Because all outputs originate from the same point cloud, consistency is maintained across different representations such as 2D floor plans, 3D floor plans or spatial analyses. This reduces rework and ensures that all derived information remains aligned with the scanned reality.
Point clouds are created by scanning a physical environment with 3D scanning technologies that measure spatial geometry and convert those measurements into digital data. During scanning, the environment is captured using devices that record distances to surfaces. These measurements are collected as raw spatial data and then processed to form a point cloud that represents the geometry of the space. Point clouds can be created using different types of scanning technologies, including:
These scanners emit laser signals and measure their return to capture distances to surfaces. They are used in both static (TLS) and mobile (SLAM-based) scanning workflows.
These scanners capture data while moving through an environment. The point cloud is reconstructed based on the scanner’s trajectory through the space.
Consumer devices equipped with LiDAR can capture 3D data for smaller indoor environments, within practical accuracy and range limits.
Regardless of the scanning device, the result of the capture process is a point cloud dataset that represents the scanned environment. In indoor mapping workflows, this point cloud is the direct output of scanning and serves as the starting point for all subsequent processing steps, including floor plan generation and spatial analysis.
Read more about the difference between photogrammetry and LiDAR in our blog article.
A wide range of point cloud formats exists, each designed to support different workflows, software ecosystems and data requirements. The choice of format influences compatibility, efficiency, storage size and long-term usability of point cloud data. Below is an overview of commonly used point cloud formats:
Beyond these examples, many additional formats exist, often tied to specific hardware manufacturers or software platforms. The variety of formats reflects the diversity of scanning technologies and use cases across surveying, construction and spatial data analysis.
Choosing the right point cloud format depends on how the data will be used, shared, stored and processed over time. Different formats have a direct impact on efficiency, compatibility, storage requirements and long-term accessibility. Key considerations when choosing a point cloud format include:
In general, formats that are binary, well-compressed and based on open standards provide the best balance between efficiency, interoperability and future readiness.
Yes, software is required to process point cloud data and transform raw scans into usable indoor mapping outputs. Scanning hardware captures spatial geometry, but this data is initially unstructured. Dedicated software is needed to interpret the point cloud, align and process the data, extract geometry, and generate structured outputs such as 2D and 3D floor plans.
Software converts raw scan data into a coherent point cloud suitable for analysis.
Walls, floors, levels and spatial relationships are derived digitally from the point cloud.
Structured deliverables such as floor plans and spatial documentation are created through software-based workflows.
Automated processing reduces manual work, minimizes rework and shortens turnaround time.
Modern indoor mapping workflows aim to consolidate processing, analysis and export in a single environment rather than relying on multiple disconnected tools.
Without dedicated software, point clouds remain raw datasets that are difficult to interpret or reuse. Indoor mapping therefore depends on software to unlock the value of scanned data and turn it into reliable, actionable outputs.
The accuracy of a point cloud is influenced by several factors, including the scanning approach, the conditions during data capture, and the processing applied after scanning.
Point clouds generated through stationary scanning benefit from stable acquisition conditions, which support consistent and reliable spatial accuracy. Mobile scanning approaches introduce additional influences, such as movement through the environment, occlusions and cumulative localisation drift, all of which can affect the resulting dataset.
Accuracy is therefore not determined by capture alone. Post-processing and optimization play a critical role in ensuring that the point cloud is suitable for reliable measurement and the generation of accurate outputs. Numeric accuracy values depend on the specific scanning setup and workflow and are not universally defined.
E57 is often chosen because it combines flexibility, openness and broad industry support in a single format.
As an open standard, E57 is not controlled by a single vendor and is maintained by a broader community. This reduces the risk of vendor lock-in and ensures long-term usability across different software platforms.
E57 supports not only 3D point data but also additional information such as images, scan data and metadata. This makes it suitable for workflows where richer scan context is required, rather than just point coordinates.
Compared to simple ASCII formats, E57 is more efficient for large datasets. Compared to many proprietary formats, it offers better interoperability. For these reasons, E57 is often a reliable default choice when exchanging or archiving point cloud data.
That said, other open formats such as LAS and LAZ are also strong options. LAZ stands out when compact file size is a priority, while E57 is particularly useful when extended scan information is needed.
Point cloud processing is the set of digital steps applied to scan data after capture to transform it into a clean, coherent and usable point cloud dataset.
Raw point clouds can contain fragmented scans, inconsistencies and unwanted data that make them harder to use directly for measurement, analysis or output generation. Processing addresses this by bringing scan data into a consistent dataset, improving data quality and preparing the point cloud for downstream use.
Depending on the goal, point cloud processing can support tasks such as generating floor plans for indoor mapping, preparing datasets for terrain monitoring, enabling reliable measurements, and making data ready for sharing or export
Point cloud registration is the process of aligning multiple scans into a single, coherent point cloud dataset.
When a building or site is scanned from different positions, each scan captures only part of the environment.
Registration ensures that these separate scans share the same spatial reference, allowing them to be combined into one unified representation of the scanned space.
It is the step that converts captured scan data into a detailed point cloud that can be processed further.
Trajectory processing in SLAM-based scanning refers to the digital processing of scan data that is captured while the scanner moves through an environment.
Unlike static scanning, mobile and SLAM-based scanning collect data continuously along a path. As the scanner moves, its position and orientation change over time, and these changes directly influence how individual measurements are placed in space. Trajectory processing reconstructs this movement so that all captured data can be positioned correctly relative to each other.
The purpose of trajectory processing is to translate movement-based capture into a coherent spatial dataset. By interpreting how the scanner moved through the environment, the processing step aligns the recorded geometry into a consistent point cloud that represents the scanned space as a whole.
Because mobile scanning depends on movement, trajectory processing plays a central role in determining the overall quality, consistency and usability of SLAM-based point clouds.
Drift correction is important because movement-based scanning does not only need to reconstruct a trajectory, but also limit the errors that inevitably arise during that movement.
As a scanner moves through an environment, small inaccuracies in position and orientation estimates can gradually accumulate. Over longer trajectories, these accumulated errors can cause visible misalignment in the point cloud, such as walls that no longer meet correctly or spaces that appear slightly distorted.
Drift correction focuses specifically on reducing these accumulated errors so that the reconstructed geometry remains consistent across the entire scanned area. This step improves the global coherence of the point cloud and helps ensure that measurements and derived outputs remain reliable, especially in larger or more complex indoor environments.
Noise and outlier removal is the process of cleaning point cloud data by identifying and removing points that do not represent meaningful geometry.
During scanning, unwanted data can be introduced by environmental conditions, reflective surfaces or measurement artifacts. These points do not correspond to real surfaces and can interfere with interpretation and analysis if left unprocessed.
Removing noise and outliers improves the overall clarity and reliability of the point cloud. A cleaner dataset is easier to process, measure, analyze and share, and provides a more dependable foundation for generating further outputs.
Noise and outlier removal is important because unprocessed point clouds can contain data that interferes with accurate measurement, analysis and visualization.
Unwanted points can obscure real geometry, introduce inconsistencies or lead to incorrect interpretations of the scanned environment. By removing noise and outliers, the point cloud becomes clearer and more reliable.
This cleaning step ensures that derived outputs - such as measurements, analyses or visual representations - are based on meaningful geometry rather than artifacts introduced during scanning. As a result, the processed dataset is better suited for further use, sharing and decision-making.
Point cloud classification is the process of grouping points within a point cloud based on shared characteristics so that different parts of the scanned environment can be identified and handled separately.
Instead of treating a point cloud as one undifferentiated mass of points, classification assigns points to logical groups. These groups typically represent different types of surfaces or elements within the scanned environment, such as structural components or distinct spatial features.
By structuring point cloud data into logical groups, classification transforms raw scan data into a dataset that is easier to manage, analyze and reuse. This makes it a valuable step in workflows that involve large or complex point clouds, where efficiency and clarity are important.
Processed point clouds are used as a foundational dataset across multiple professional application areas. Once scanning data has been processed and optimized, it can support a wide range of spatial workflows.
Generation of accurate 2D and 3D floor plans. Creation of structured spatial documentation of interior environments. Extraction of measurements and layouts from scanned geometry.
Analysis of landscapes and terrain surfaces. Monitoring changes over time, such as volume or surface differences. Preparation of datasets for reporting, sharing and export to other platforms.
Reliable spatial measurement across buildings or sites. Extraction of geometry for analysis and documentation. Use of scanned data as a consistent reference for surveying workflows.
Across all these applications, processed point clouds provide a consistent and reusable spatial reference that supports accurate analysis, documentation and decision-making.
Point cloud registration matters because it determines whether scanned data can be used reliably.
Without registration, individual scans remain isolated fragments that cannot be measured or analyzed consistently. Registration ensures that spatial relationships - such as distances and relative positions - are preserved across the entire dataset.
Registration is a prerequisite for all downstream workflows, including processing, optimization, indoor mapping and terrain analysis.
Point cloud registration is important for surveyors because it ensures that scanned data forms a single, spatially consistent dataset that can be used for reliable measurement, analysis and documentation.
Surveying workflows depend on correct spatial relationships between all measured elements. When scans are captured from multiple positions, registration aligns these scans into a common reference so that distances, angles and relative positions are preserved across the entire dataset. Without proper registration, measurements taken from different parts of the scan would not be comparable or trustworthy.
For surveyors, registration is therefore a foundational step that enables:
While specific surveying standards or tolerances depend on project context and tools, registration is universally required to turn raw scan data into a dataset that can support professional surveying tasks.
Indoor mapping is used to create structured and measurable digital representations of interior spaces based on scanned data. Rather than documenting spaces through selective measurements or manual drawings, indoor mapping captures the full geometry of a building and converts it into reusable digital outputs.
These outputs form the basis for multiple downstream applications, such as floor plan generation, spatial documentation and analysis. Because all applications rely on the same underlying scanned dataset, indoor mapping provides a consistent reference for different use cases across the lifecycle of a building.
Indoor mapping captures the real geometry of existing buildings by recording interior spaces as scanned data instead of relying on existing drawings or assumptions.
By scanning the building as it exists, indoor mapping documents actual dimensions, spatial relationships and layout conditions, including deviations from original plans. This approach is particularly valuable when existing drawings are missing, outdated or no longer reflect the current state of the building.
The resulting point cloud serves as a direct representation of the scanned environment, from which geometry is extracted digitally.
Indoor mapping generates accurate floor plans by extracting building geometry directly from point cloud data captured during scanning. Instead of relying on manual measurements or interpreted drawings, spatial information is derived from the scanned representation of the building as it exists. From the point cloud, structured geometry such as walls, floors, rooms and levels can be identified and converted into vectorized outputs. These outputs form the basis for both 2D and 3D floor plans, ensuring that layouts reflect real dimensions and spatial relationships rather than assumptions.
In more advanced workflows, the same scan-based geometry can also support the creation of sections and elevations, as these are simply different views or slices through the same spatial dataset. Whether producing plans, sections or 3D representations, indoor mapping ensures that all outputs are grounded in the same scanned geometry.
Indoor mapping supports facility management by providing a digital spatial reference of a building that can be used to manage spaces, assets, and maintenance activities more efficiently. Facility management relies on accurate knowledge of how a building is laid out and how spaces relate to each other. Indoor mapping captures this information directly from the existing building and makes it available as a reusable digital dataset.
Indoor mapping delivers up-to-date floor plans and spatial layouts that reflect the actual condition of the building. This helps facility managers understand spaces, circulation and relationships between rooms and zones.
Spatial data provides context for locating assets such as equipment, installations and infrastructure. This supports planning of maintenance activities and improves communication between teams working in different areas of the building.
Indoor mapping enables reliable space measurements and layout analysis, which can support decisions around space allocation, usage and reconfiguration over time.
Many facility management workflows depend on drawings that are incomplete or no longer accurate. Indoor mapping replaces these with scan-based documentation that reflects the building as it exists.
Once captured, the indoor mapping dataset can be reused for multiple facility management tasks without requiring repeated site surveys, reducing operational effort over the building’s lifecycle.
In facility management, indoor mapping functions as a continuously valuable reference that supports informed decision-making, coordination and long-term building operations.
In scan-to-BIM workflows, indoor mapping is used as the bridge between raw scan data and structured BIM models. The process typically starts with scanning an existing building to capture its interior geometry as a point cloud. Indoor mapping organizes and structures this scan data so it can be interpreted reliably before BIM modeling begins.
In summary, indoor mapping enables scan-to-BIM workflows by transforming raw scan data into a usable spatial reference that supports accurate, efficient and reliable BIM modeling of existing buildings.
Indoor mapping is particularly efficient for large and complex buildings because it captures the entire interior geometry as a single digital dataset that can be reused across multiple workflows.
As building size and complexity increase, the efficiency gains of indoor mapping become more significant, making it a scalable approach for documenting and managing large interior environments.
Indoor mapping supports renovation and retrofit projects by providing reliable base data that reflects the existing condition of a building.
Because geometry is captured from scans rather than drawings, indoor mapping delivers a dependable reference for planning and design. This reduces uncertainty caused by incomplete or inaccurate documentation and allows design and construction decisions to be based on measured spatial data.
Indoor mapping reduces rework and shortens project timelines by starting from accurate, up-to-date building geometry.
Indoor mapping is used in real estate and property documentation to capture interior spaces efficiently and generate structured outputs such as floor plans.
Scanning-based workflows enable fast measurement and documentation of properties, supporting tasks such as space calculation, valuation and due diligence. By deriving documentation from scanned geometry, indoor mapping helps ensure consistency between different representations of the same property.
This guide provides a complete foundation for understanding indoor mapping - from scanning approaches and point clouds to processing and practical applications. But indoor mapping is a broad domain, and different projects often require deeper insight into specific topics. If you want to explore indoor mapping further, the following areas are natural next steps:
SLAM (Simultaneous Localization and Mapping) is revolutionizing 3D mapping for surveyors, enabling fast and accurate results in GPS-deprived environments. This guide covers SLAM fundamentals, workflow optimization, and its advantages over traditional Terrestrial Laser Scanning (TLS). Discover how SLAM technology enhances efficiency, versatility, and accuracy in modern surveying.
Indoor mapping has become a foundational technology for surveying and construction workflows involving existing buildings. By capturing interior environments as scan-based geometry, indoor mapping provides a reliable spatial reference that can be reused across applications such as floor plan generation, renovation and retrofit projects, scan-to-BIM workflows, real estate documentation and facility management.
Point clouds are the foundation of modern surveying and indoor mapping. This article explains how point clouds are created, processed and validated to achieve accurate, survey-grade results, and why proper processing is essential for reliable measurements, floor plans and professional applications.
Each of these topics builds on the fundamentals explained in this guide and helps translate indoor mapping knowledge into confident, project-ready decisions.
Indoor mapping shifts building documentation from manual interpretation to measured reality. By understanding how scans, point clouds and processing fit together, you can reduce uncertainty, avoid rework and make better use of spatial data across your projects.
This guide is designed to be a reference you can return to, whether you’re starting with indoor scanning or refining existing workflows.
If you’d like a structured overview, you can also download our free white paper on indoor mapping.
