Spatial mapping - Revision history

Xinreality at 00:46, 28 October 2025

2025-10-28T00:46:06Z

Xinreality: Text replacement - "e.g.," to "for example"

2025-10-28T00:29:36Z

Text replacement - "e.g.," to "for example"

Xinreality at 22:48, 27 October 2025

2025-10-27T22:48:31Z

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	[[File:spatial mapping2.jpg\|300px\|right]]		[[File:spatial mapping2.jpg\|300px\|right]]
	'''Spatial mapping''', also known as '''3D reconstruction''' in some contexts, is a core technology that enables a device to create a three-dimensional (3D) digital model of its physical environment in real-time.<ref name="StereolabsDocsS2">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="ZaubarLexicon">{{cite web \|url=https://about.zaubar.com/en/xr-ai-lexicon/spatial-mapping \|title=Spatial Mapping \|publisher=Zaubar \|access-date=2025-10-23}}</ref> It is a fundamental component of [[augmented reality]] (AR), [[virtual reality]] (VR), [[mixed reality]] (MR), and [[robotics]], allowing systems to perceive, understand, and interact with the physical world.<ref name="StereolabsDocsS1">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="EducativeIO"~~>{{cite web \|url=https:~~/~~/www.educative.io/answers/spatial-mapping-and-3d-reconstruction-in-augmented-reality \|title=Spatial mapping and 3D reconstruction in augmented reality \|publisher=Educative \|access-date=2023}}</ref~~> By creating a detailed digital map of surfaces, objects, and their spatial relationships, spatial mapping serves as the technological bridge between the digital and physical realms, allowing for the realistic blending of virtual and real worlds.<ref name="StereolabsDocsS1"/>		'''Spatial mapping''', also known as '''3D reconstruction''' in some contexts, is a core technology that enables a device to create a three-dimensional (3D) digital model of its physical environment in real-time.<ref name="StereolabsDocsS2">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="ZaubarLexicon">{{cite web \|url=https://about.zaubar.com/en/xr-ai-lexicon/spatial-mapping \|title=Spatial Mapping \|publisher=Zaubar \|access-date=2025-10-23}}</ref> It is a fundamental component of [[augmented reality]] (AR), [[virtual reality]] (VR), [[mixed reality]] (MR), and [[robotics]], allowing systems to perceive, understand, and interact with the physical world.<ref name="StereolabsDocsS1">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="EducativeIO"/> By creating a detailed digital map of surfaces, objects, and their spatial relationships, spatial mapping serves as the technological bridge between the digital and physical realms, allowing for the realistic blending of virtual and real worlds.<ref name="StereolabsDocsS1"/>

	The process is dynamic and continuous; a device equipped for spatial mapping constantly scans its surroundings with a suite of sensors, building and refining its 3D map over time by incorporating new depth and positional data as it moves through an environment.<ref name="StereolabsDocsS2"/><ref name="UnityDocs">{{cite web \|url=https://docs.unity3d.com/2019.1/Documentation/Manual/SpatialMapping.html \|title=Spatial Mapping concepts \|publisher=Unity \|access-date=2025-10-23}}</ref> This capability is foundational to the field of [[extended reality]] (XR), enabling applications to place digital content accurately, facilitate realistic physical interactions like [[occlusion]] and collision, and provide environmental context for immersive experiences.<ref name="ZaubarLexicon"/><ref name="EducativeIO"/>		The process is dynamic and continuous; a device equipped for spatial mapping constantly scans its surroundings with a suite of sensors, building and refining its 3D map over time by incorporating new depth and positional data as it moves through an environment.<ref name="StereolabsDocsS2"/><ref name="UnityDocs">{{cite web \|url=https://docs.unity3d.com/2019.1/Documentation/Manual/SpatialMapping.html \|title=Spatial Mapping concepts \|publisher=Unity \|access-date=2025-10-23}}</ref> This capability is foundational to the field of [[extended reality]] (XR), enabling applications to place digital content accurately, facilitate realistic physical interactions like [[occlusion]] and collision, and provide environmental context for immersive experiences.<ref name="ZaubarLexicon"/><ref name="EducativeIO"/>

Xinreality: /* Future Directions */

2025-10-27T22:46:00Z

Future Directions

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	=== Semantic Spatial Understanding ===		=== Semantic Spatial Understanding ===

	The next major frontier for spatial mapping is the shift from purely geometric understanding (knowing ''where'' a surface is) to '''semantic understanding''' (knowing ''what'' a surface is).<ref name="SpatialAI"/><ref name="FutureDirections1">{{cite web \|url=https://arxiv.org/html/2508.20477v1 \|title=What is Spatial Computing? A Survey on the Foundations and State-of-the-Art \|publisher=arXiv \|access-date=2025-10-23}}</ref> This involves leveraging [[AI]] and [[machine learning]] algorithms to analyze the map data and automatically identify, classify, and label objects and architectural elements in real-time—for example, recognizing a surface as a "couch," an opening as a "door," or an object as a "chair."<ref name="MetaHelp"/><ref name="SpatialAI"/>		The next major frontier for spatial mapping is the shift from purely geometric understanding (knowing ''where'' a surface is) to '''[[semantic understanding]]''' (knowing ''what'' a surface is).<ref name="SpatialAI"/><ref name="FutureDirections1">{{cite web \|url=https://arxiv.org/html/2508.20477v1 \|title=What is Spatial Computing? A Survey on the Foundations and State-of-the-Art \|publisher=arXiv \|access-date=2025-10-23}}</ref> This involves leveraging [[AI]] and [[machine learning]] algorithms to analyze the map data and automatically identify, classify, and label objects and architectural elements in real-time—for example, recognizing a surface as a "couch," an opening as a "door," or an object as a "chair."<ref name="MetaHelp"/><ref name="SpatialAI"/>

	This capability, already emerging in platforms like Meta Quest's Scene API, will enable a new generation of intelligent and context-aware XR experiences. Virtual characters could realistically interact with the environment (e.g., sitting on a recognized couch), applications could automatically adapt their UI to the user's specific room layout, and digital assistants could understand commands related to physical objects ("place the virtual screen on that wall").<ref name="FutureDirections1"/>		This capability, already emerging in platforms like Meta Quest's Scene API, will enable a new generation of intelligent and context-aware XR experiences. Virtual characters could realistically interact with the environment (e.g., sitting on a recognized couch), applications could automatically adapt their UI to the user's specific room layout, and digital assistants could understand commands related to physical objects ("place the virtual screen on that wall").<ref name="FutureDirections1"/>
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	=== Neural Rendering and AI-Powered Mapping ===		=== Neural Rendering and AI-Powered Mapping ===

	Neural Radiance Fields (NeRF) revolutionized 3D scene representation when introduced by UC Berkeley researchers in March 2020, representing continuous volumetric scene function producing photorealistic novel views through neural network. Key variants address limitations: Instant-NGP (2022) reduces training from hours to seconds through multi-resolution hash encoding, while Mip-NeRF (2021) adds anti-aliasing for better rendering at multiple scales.<ref name="nerf">{{cite web \|url=https://www.matthewtancik.com/nerf \|title=NeRF: Neural Radiance Fields \|publisher=UC Berkeley \|access-date=2025-10-27}}</ref>		[[Neural Radiance Fields]] (NeRF) revolutionized 3D scene representation when introduced by UC Berkeley researchers in March 2020, representing continuous volumetric scene function producing photorealistic novel views through neural network. Key variants address limitations: Instant-NGP (2022) reduces training from hours to seconds through multi-resolution hash encoding, while Mip-NeRF (2021) adds anti-aliasing for better rendering at multiple scales.<ref name="nerf">{{cite web \|url=https://www.matthewtancik.com/nerf \|title=NeRF: Neural Radiance Fields \|publisher=UC Berkeley \|access-date=2025-10-27}}</ref>

	3D Gaussian Splatting emerged in August 2023 as breakthrough achieving real-time performance at 30+ fps for 1080p rendering—100 to 1000 times faster than NeRF. The technique represents scenes using millions of 3D Gaussians in explicit representation versus NeRF's implicit neural encoding, enabling real-time rendering crucial for interactive AR/VR applications.<ref name="gaussian">{{cite web \|url=https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ \|title=3D Gaussian Splatting for Real-Time Radiance Field Rendering \|publisher=INRIA \|access-date=2025-10-27}}</ref>		3D Gaussian Splatting emerged in August 2023 as breakthrough achieving real-time performance at 30+ fps for 1080p rendering—100 to 1000 times faster than NeRF. The technique represents scenes using millions of 3D Gaussians in explicit representation versus NeRF's implicit neural encoding, enabling real-time rendering crucial for interactive AR/VR applications.<ref name="gaussian">{{cite web \|url=https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ \|title=3D Gaussian Splatting for Real-Time Radiance Field Rendering \|publisher=INRIA \|access-date=2025-10-27}}</ref>
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	=== The Role of Edge Computing and the Cloud ===		=== The Role of Edge Computing and the Cloud ===

	To overcome the processing and power limitations of mobile XR devices, computationally intensive spatial mapping tasks will increasingly be offloaded to the network edge or the cloud.<ref name="AdeiaBlog">{{cite web \|url=https://adeia.com/blog/spatial-mapping-empowering-the-future-of-ar \|title=Spatial Mapping: Empowering the Future of AR \|publisher=Adeia \|access-date=2025-10-23}}</ref> In this '''split-compute''' model, a lightweight headset would be responsible for capturing raw sensor data and sending it to a powerful nearby edge server. The server would then perform the heavy lifting—running SLAM algorithms, generating the mesh, and performing semantic analysis—and stream the resulting map data back to the device with extremely low latency.<ref name="AdeiaBlog"/>		To overcome the processing and power limitations of mobile XR devices, computationally intensive spatial mapping tasks will increasingly be offloaded to the network edge or the cloud.<ref name="AdeiaBlog">{{cite web \|url=https://adeia.com/blog/spatial-mapping-empowering-the-future-of-ar \|title=Spatial Mapping: Empowering the Future of AR \|publisher=Adeia \|access-date=2025-10-23}}</ref> In this '''[[split-compute]]''' model, a lightweight headset would be responsible for capturing raw sensor data and sending it to a powerful nearby edge server. The server would then perform the heavy lifting—running SLAM algorithms, generating the mesh, and performing semantic analysis—and stream the resulting map data back to the device with extremely low latency.<ref name="AdeiaBlog"/>

	Furthermore, the cloud will play a crucial role in creating and hosting large-scale, persistent spatial maps, often referred to as '''[[digital twin]]s''' or the '''AR Cloud'''. By aggregating and merging map data from many users, it will be possible to build and maintain a shared, persistent digital replica of real-world locations, enabling multi-user experiences at an unprecedented scale.<ref name="MagicLeapLegal"/><ref name="AdeiaBlog"/>		Furthermore, the cloud will play a crucial role in creating and hosting large-scale, persistent spatial maps, often referred to as '''[[digital twin]]s''' or the '''[[AR Cloud]]'''. By aggregating and merging map data from many users, it will be possible to build and maintain a shared, persistent digital replica of real-world locations, enabling multi-user experiences at an unprecedented scale.<ref name="MagicLeapLegal"/><ref name="AdeiaBlog"/>

	=== Standardization and Interoperability ===		=== Standardization and Interoperability ===

Xinreality: /* Sensor and Algorithmic Constraints */

2025-10-27T22:45:08Z

Sensor and Algorithmic Constraints

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	* '''Problematic Surfaces''': Onboard sensors often struggle with certain types of materials. Transparent surfaces like glass, highly reflective surfaces like mirrors, and textureless or dark, light-absorbing surfaces can fail to return usable data to depth sensors, resulting in gaps or inaccuracies in the map.<ref name="UnityDocs"/><ref name="HoloLensSpaces"/><ref name="MagicLeapMappingDocs">{{cite web \|url=https://developer-docs.magicleap.cloud/docs/guides/features/spatial-mapping/ \|title=Real-time World Sensing \|publisher=Magic Leap \|access-date=2025-10-23}}</ref>		* '''Problematic Surfaces''': Onboard sensors often struggle with certain types of materials. Transparent surfaces like glass, highly reflective surfaces like mirrors, and textureless or dark, light-absorbing surfaces can fail to return usable data to depth sensors, resulting in gaps or inaccuracies in the map.<ref name="UnityDocs"/><ref name="HoloLensSpaces"/><ref name="MagicLeapMappingDocs">{{cite web \|url=https://developer-docs.magicleap.cloud/docs/guides/features/spatial-mapping/ \|title=Real-time World Sensing \|publisher=Magic Leap \|access-date=2025-10-23}}</ref>

	* '''Drift''': Tracking systems that rely on [[odometry]] (estimating motion from sensor data) are susceptible to small, accumulating errors over time. This phenomenon, known as '''drift''', can cause the digital map to become misaligned with the real world. While algorithms use techniques like [[loop closure]] to correct for drift, it can still be a significant problem in large, feature-poor environments (like a long, white hallway).<ref name="MilvusSLAM"/><ref name="SLAMSystems"/>		* '''[[Drift]]''': Tracking systems that rely on [[odometry]] (estimating motion from sensor data) are susceptible to small, accumulating errors over time. This phenomenon, known as '''drift''', can cause the digital map to become misaligned with the real world. While algorithms use techniques like [[loop closure]] to correct for drift, it can still be a significant problem in large, feature-poor environments (like a long, white hallway).<ref name="MilvusSLAM"/><ref name="SLAMSystems"/>

	* '''Scale and Boundaries''': The way spatial data is aggregated and defined can influence analytical results, a concept known in geography as the [[Modifiable Areal Unit Problem]] (MAUP). This problem highlights that statistical outcomes can change based on the shape and scale of the zones used for analysis, which has parallels in how room-scale maps are chunked and interpreted.<ref name="MAUP1">{{cite web \|url=https://pmc.ncbi.nlm.nih.gov/articles/PMC7254930/ \|title=The modifiable areal unit problem in ecological community data \|publisher=PLOS ONE \|access-date=2025-10-23}}</ref><ref name="MAUP2">{{cite web \|url=https://zenn-wong.medium.com/the-challenges-of-using-maps-in-policy-making-510e3fcb8eb3 \|title=The Challenges of Using Maps in Policy-Making \|publisher=Medium \|access-date=2025-10-23}}</ref>		* '''Scale and Boundaries''': The way spatial data is aggregated and defined can influence analytical results, a concept known in geography as the [[Modifiable Areal Unit Problem]] (MAUP). This problem highlights that statistical outcomes can change based on the shape and scale of the zones used for analysis, which has parallels in how room-scale maps are chunked and interpreted.<ref name="MAUP1">{{cite web \|url=https://pmc.ncbi.nlm.nih.gov/articles/PMC7254930/ \|title=The modifiable areal unit problem in ecological community data \|publisher=PLOS ONE \|access-date=2025-10-23}}</ref><ref name="MAUP2">{{cite web \|url=https://zenn-wong.medium.com/the-challenges-of-using-maps-in-policy-making-510e3fcb8eb3 \|title=The Challenges of Using Maps in Policy-Making \|publisher=Medium \|access-date=2025-10-23}}</ref>

Xinreality: /* Meta Quest */

2025-10-27T22:44:25Z

Meta Quest

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	=== Meta Quest ===		=== Meta Quest ===

	Meta Quest evolved from pure virtual reality to sophisticated mixed reality through progressive spatial mapping capabilities. Quest 3 launched in 2023 with revolutionary spatial capabilities, featuring Snapdragon XR2 Gen 2 providing 2× GPU performance versus Quest 2, dual LCD displays at 2064×2208 per eye, and sophisticated sensor array including two 4MP RGB color cameras for full-color passthrough, four hybrid monochrome/IR cameras for tracking, and one IR patterned light emitter serving as depth sensor.<ref name="MetaHelp"/>		[[Meta Quest]] evolved from pure virtual reality to sophisticated mixed reality through progressive spatial mapping capabilities. Quest 3 launched in 2023 with revolutionary spatial capabilities, featuring Snapdragon XR2 Gen 2 providing 2× GPU performance versus Quest 2, dual LCD displays at 2064×2208 per eye, and sophisticated sensor array including two 4MP RGB color cameras for full-color passthrough, four hybrid monochrome/IR cameras for tracking, and one IR patterned light emitter serving as depth sensor.<ref name="MetaHelp"/>

	The Scene API enables semantic understanding of physical environments through system-generated scene models with semantic labels including floor, ceiling, walls, desk, couch, table, window, and lamp. The API provides bounded 2D entities defining surfaces like walls and floors with 2D boundaries and bounding boxes, bounded 3D entities for objects like furniture with 3D bounding boxes, and room layout with automatic room structure detection and classification.<ref name="MetaHelp"/>		The Scene API enables semantic understanding of physical environments through system-generated scene models with semantic labels including floor, ceiling, walls, desk, couch, table, window, and lamp. The API provides bounded 2D entities defining surfaces like walls and floors with 2D boundaries and bounding boxes, bounded 3D entities for objects like furniture with 3D bounding boxes, and room layout with automatic room structure detection and classification.<ref name="MetaHelp"/>

Xinreality: /* Google ARCore */

2025-10-27T22:44:09Z

Google ARCore

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	=== Google ARCore ===		=== Google ARCore ===

	Google ARCore launched in 2017 as the company's platform-agnostic augmented reality SDK, providing cross-platform APIs for Android, iOS, Unity, and Web after discontinuing the hardware-dependent Project Tango. ARCore achieves spatial understanding without specialized sensors through depth-from-motion algorithms that compare multiple device images from different angles, combining visual information with IMU measurements running at 1000 Hz. The system performs motion tracking at 60 fps using Simultaneous Localization and Mapping with visual and inertial data fusion.<ref name="arcore">{{cite web \|url=https://developers.google.com/ar \|title=ARCore Overview \|publisher=Google Developers \|access-date=2025-10-27}}</ref>		[[Google ARCore]] launched in 2017 as the company's platform-agnostic augmented reality SDK, providing cross-platform APIs for Android, iOS, Unity, and Web after discontinuing the hardware-dependent Project Tango. ARCore achieves spatial understanding without specialized sensors through depth-from-motion algorithms that compare multiple device images from different angles, combining visual information with IMU measurements running at 1000 Hz. The system performs motion tracking at 60 fps using Simultaneous Localization and Mapping with visual and inertial data fusion.<ref name="arcore">{{cite web \|url=https://developers.google.com/ar \|title=ARCore Overview \|publisher=Google Developers \|access-date=2025-10-27}}</ref>

	The Depth API public launch in ARCore 1.18 (June 2020) brought occlusion capabilities to hundreds of millions of compatible Android devices. The depth-from-motion algorithm creates depth images using RGB camera and device movement, selectively using machine learning to increase depth processing even with minimal motion. Depth images store 16-bit unsigned integers per pixel representing distance from camera to environment, with depth range of 0 to 65 meters and most accurate results from 0.5 to 5 meters from real-world scenes.<ref name="RoadtoVR"/>		The Depth API public launch in ARCore 1.18 (June 2020) brought occlusion capabilities to hundreds of millions of compatible Android devices. The depth-from-motion algorithm creates depth images using RGB camera and device movement, selectively using machine learning to increase depth processing even with minimal motion. Depth images store 16-bit unsigned integers per pixel representing distance from camera to environment, with depth range of 0 to 65 meters and most accurate results from 0.5 to 5 meters from real-world scenes.<ref name="RoadtoVR"/>

Xinreality: /* Apple ARKit */

2025-10-27T22:43:17Z

Apple ARKit

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	=== Apple ARKit ===		=== Apple ARKit ===

	Apple ARKit democratized spatial mapping by bringing sophisticated AR capabilities to hundreds of millions of iOS devices without requiring specialized hardware. ARKit 1 launched in 2017 with iOS 11, providing basic horizontal plane detection, Visual Inertial Odometry for tracking, and scene understanding on devices with A9 processors or later.<ref name="AndreasJakl"/>		[[Apple ARKit]] democratized spatial mapping by bringing sophisticated AR capabilities to hundreds of millions of iOS devices without requiring specialized hardware. ARKit 1 launched in 2017 with iOS 11, providing basic horizontal plane detection, Visual Inertial Odometry for tracking, and scene understanding on devices with A9 processors or later.<ref name="AndreasJakl"/>

	ARKit 3.5 in iOS 13.4 (March 2020) marked a revolutionary leap with the Scene Geometry API, powered by LiDAR on iPad Pro 4th generation. This first LiDAR-powered spatial mapping provided instant plane detection without scanning, triangle mesh reconstruction with classification into semantic categories (wall, floor, ceiling, table, seat, window, door), enhanced raycasting with scene geometry, and per-pixel depth information through the Depth API. The system could exclude people from reconstructed meshes and provided effective range up to 5 meters.<ref name="AppleDeveloper"/>		ARKit 3.5 in iOS 13.4 (March 2020) marked a revolutionary leap with the Scene Geometry API, powered by LiDAR on iPad Pro 4th generation. This first LiDAR-powered spatial mapping provided instant plane detection without scanning, triangle mesh reconstruction with classification into semantic categories (wall, floor, ceiling, table, seat, window, door), enhanced raycasting with scene geometry, and per-pixel depth information through the Depth API. The system could exclude people from reconstructed meshes and provided effective range up to 5 meters.<ref name="AppleDeveloper"/>

Xinreality: /* Foundational Algorithms */

2025-10-27T22:42:12Z

Foundational Algorithms

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	Depending on the primary sensors used, SLAM can be categorized into several types:		Depending on the primary sensors used, SLAM can be categorized into several types:

	* '''Visual SLAM (vSLAM)''': Uses one or more cameras to track visual features.<ref name="MathWorksSLAM"/>		* '''[[Visual SLAM]] (vSLAM)''': Uses one or more cameras to track visual features.<ref name="MathWorksSLAM"/>
	* '''LiDAR SLAM''': Uses a LiDAR sensor to build a precise geometric map.<ref name="MathWorksSLAM"/>		* '''[[LiDAR SLAM]]''': Uses a LiDAR sensor to build a precise geometric map.<ref name="MathWorksSLAM"/>
	* '''Multi-Sensor SLAM''': Fuses data from various sources (e.g., cameras, IMU, LiDAR) for enhanced robustness and accuracy.<ref name="MathWorksSLAM"/>		* '''[[Multi-Sensor SLAM]]''': Fuses data from various sources (e.g., cameras, IMU, LiDAR) for enhanced robustness and accuracy.<ref name="MathWorksSLAM"/>

	Spatial mapping is typically accomplished via SLAM algorithms, which build a map of the environment in real time while tracking the device's position within it.<ref name="Adeia">{{cite web \|url=https://adeia.com/blog/spatial-mapping-empowering-the-future-of-ar \|title=Spatial Mapping: Empowering the Future of AR \|publisher=Adeia \|date=2022-03-02 \|access-date=2025-10-27}}</ref>		Spatial mapping is typically accomplished via SLAM algorithms, which build a map of the environment in real time while tracking the device's position within it.<ref name="Adeia">{{cite web \|url=https://adeia.com/blog/spatial-mapping-empowering-the-future-of-ar \|title=Spatial Mapping: Empowering the Future of AR \|publisher=Adeia \|date=2022-03-02 \|access-date=2025-10-27}}</ref>

Xinreality: /* RGB Cameras */

2025-10-27T22:40:51Z

RGB Cameras

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	==== RGB Cameras ====		==== RGB Cameras ====

	Standard color cameras capture visual information such as color and texture. This data is crucial for texturing the 3D mesh to create a photorealistic model.<ref name="ZaubarLexicon"/> Additionally, the images from RGB cameras are used by [[computer vision]] algorithms to track visual features in the environment, which is the basis of '''Visual SLAM (vSLAM)'''.<ref name="MathWorksSLAM"/>		Standard color cameras capture visual information such as color and texture. This data is crucial for texturing the 3D mesh to create a photorealistic model.<ref name="ZaubarLexicon"/> Additionally, the images from RGB cameras are used by [[computer vision]] algorithms to track visual features in the environment, which is the basis of '''[[Visual SLAM]] (vSLAM)'''.<ref name="MathWorksSLAM"/>

	==== Inertial Measurement Units (IMUs) ====		==== Inertial Measurement Units (IMUs) ====

← Older revision		Revision as of 22:48, 27 October 2025
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	[[File:spatial mapping2.jpg\|300px\|right]]		[[File:spatial mapping2.jpg\|300px\|right]]
	'''Spatial mapping''', also known as '''3D reconstruction''' in some contexts, is a core technology that enables a device to create a three-dimensional (3D) digital model of its physical environment in real-time.<ref name="StereolabsDocsS2">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="ZaubarLexicon">{{cite web \|url=https://about.zaubar.com/en/xr-ai-lexicon/spatial-mapping \|title=Spatial Mapping \|publisher=Zaubar \|access-date=2025-10-23}}</ref> It is a fundamental component of [[augmented reality]] (AR), [[virtual reality]] (VR), [[mixed reality]] (MR), and [[robotics]], allowing systems to perceive, understand, and interact with the physical world.<ref name="StereolabsDocsS1">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="EducativeIO"~~>{{cite web \|url=https:~~/~~/www.educative.io/answers/spatial-mapping-and-3d-reconstruction-in-augmented-reality \|title=Spatial mapping and 3D reconstruction in augmented reality \|publisher=Educative \|access-date=2023}}</ref~~> By creating a detailed digital map of surfaces, objects, and their spatial relationships, spatial mapping serves as the technological bridge between the digital and physical realms, allowing for the realistic blending of virtual and real worlds.<ref name="StereolabsDocsS1"/>		'''Spatial mapping''', also known as '''3D reconstruction''' in some contexts, is a core technology that enables a device to create a three-dimensional (3D) digital model of its physical environment in real-time.<ref name="StereolabsDocsS2">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="ZaubarLexicon">{{cite web \|url=https://about.zaubar.com/en/xr-ai-lexicon/spatial-mapping \|title=Spatial Mapping \|publisher=Zaubar \|access-date=2025-10-23}}</ref> It is a fundamental component of [[augmented reality]] (AR), [[virtual reality]] (VR), [[mixed reality]] (MR), and [[robotics]], allowing systems to perceive, understand, and interact with the physical world.<ref name="StereolabsDocsS1">{{cite web \|url=https://www.stereolabs.com/docs/spatial-mapping \|title=Spatial Mapping Overview \|publisher=Stereolabs \|access-date=2025-10-23}}</ref><ref name="EducativeIO"/> By creating a detailed digital map of surfaces, objects, and their spatial relationships, spatial mapping serves as the technological bridge between the digital and physical realms, allowing for the realistic blending of virtual and real worlds.<ref name="StereolabsDocsS1"/>

	The process is dynamic and continuous; a device equipped for spatial mapping constantly scans its surroundings with a suite of sensors, building and refining its 3D map over time by incorporating new depth and positional data as it moves through an environment.<ref name="StereolabsDocsS2"/><ref name="UnityDocs">{{cite web \|url=https://docs.unity3d.com/2019.1/Documentation/Manual/SpatialMapping.html \|title=Spatial Mapping concepts \|publisher=Unity \|access-date=2025-10-23}}</ref> This capability is foundational to the field of [[extended reality]] (XR), enabling applications to place digital content accurately, facilitate realistic physical interactions like [[occlusion]] and collision, and provide environmental context for immersive experiences.<ref name="ZaubarLexicon"/><ref name="EducativeIO"/>		The process is dynamic and continuous; a device equipped for spatial mapping constantly scans its surroundings with a suite of sensors, building and refining its 3D map over time by incorporating new depth and positional data as it moves through an environment.<ref name="StereolabsDocsS2"/><ref name="UnityDocs">{{cite web \|url=https://docs.unity3d.com/2019.1/Documentation/Manual/SpatialMapping.html \|title=Spatial Mapping concepts \|publisher=Unity \|access-date=2025-10-23}}</ref> This capability is foundational to the field of [[extended reality]] (XR), enabling applications to place digital content accurately, facilitate realistic physical interactions like [[occlusion]] and collision, and provide environmental context for immersive experiences.<ref name="ZaubarLexicon"/><ref name="EducativeIO"/>