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Photogrammetry

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Photogrammetry is the process of creating a realistic 3D scene by taking a series of photographs and combining them using specialized software. The software takes measurements from conventional digital photographs to find the exact position of surface points. This technology enables the production of precise 3D models of real subjects for scientific simulations, entertainment, medicine, or archiving. The American Society for Photogrammetry and Remote Sensing (ASPRS) defines the field more broadly as "the art, science and technology of obtaining reliable information about physical objects and the environment through processes of recording measuring and interpreting images and patterns of electromagnetic radiant energy and other phenomena".[1]

The discipline dates to the 19th century. The French officer Aimé Laussedat used photographs for topographic surveys as early as 1861, and the word itself was first printed in German, as Photogrammetrie, in December 1867.[2][3] Its core mathematics, including bundle adjustment, was developed for aerial mapping and later adopted by computer vision, where automated structure from motion made it possible to build models from photographs taken with ordinary consumer cameras.[4][5]

In virtual reality, photogrammetry is used to put real places into headsets, where room-scale tracking lets users walk around a captured scene. Valve published a three-part guide to photogrammetry for VR in 2015 and shipped scanned environments in its own demos and in The Lab.[6][7] In augmented reality, Apple's Object Capture API turns photos of an object into 3D models for AR apps.[8] The camera poses and sparse points that photogrammetric pipelines compute were also the starting input for the original papers on neural radiance fields and Gaussian splatting.[9][10]

Reviewed 6 October 2026. Checked the history, method and software-status claims (incl. RealityScan 2.0 to 2.2, 3DF Zephyr pricing) and the VR/AR application facts against the cited papers, Valve's 2015 guide, vendor pages and press articles. About review dates.

General objective

The general objective is to capture the position of every point in the scene by using at least two photographs taken from different angles, whether by one camera that is moved between shots or by several cameras.[6] Dynamic scenes such as people or animals in motion can be captured with a complex multi-camera setup; the free-viewpoint video system described by Microsoft researchers in 2015, for example, records performances with a dense set of synchronized RGB and infrared video cameras.[11] The resulting form of the subject is then calculated via specialized software such as Agisoft PhotoScan, Autodesk 123D Catch, or Autodesk Memento, the three packages Valve named in its 2015 guide.[6]

Photogrammetry is one of several ways to digitize real objects, alongside active 3D scanning methods such as laser scanning. Westoby and colleagues compared an elevation model built with structure-from-motion photogrammetry from consumer-camera photographs against a terrestrial laser scan and found that decimetre-scale vertical accuracy can be achieved even at sites with complex topography and a range of land covers.[5]

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Photogrammetry in SteamVR

History

Origins and the name

Several 19th-century engineers and photographers worked on using photographs for surveying, under competing names. Albrecht Grimm's history of the term lists Ikonometrie and Metrophotographie (Laussedat), Photometrographie, Photogrammetrie and Messbildkunst (Meydenbauer), Photographometrie (Porro) and Phototopographie (Pujo and Fourcade), among others.[3]

Aimé Laussedat (1819-1907) is often called the "father of photogrammetry".[2][12] In a 2020 paper, L. Polidori writes that Laussedat used photographic images for topographic surveys as early as 1861, with a method he called metrophotography, but argues that his contribution was mainly instrumental: it left out error computation, stereoscopy and aerial photography, and early 20th-century analog photogrammetry did not grow directly out of his techniques.[2]

In Germany, the civil engineer Albrecht Meydenbauer worked on photogrammetric methods from 1858, aiming at surveys of historic buildings. According to Grimm, the geographer Otto Kersten, whom Meydenbauer had contacted about using the method on expeditions, proposed changing the awkward wording Photographometrie to Photogrammetrie. The new word first appeared on 6 December 1867 as the title of an unsigned article, "Die Photogrammetrie", in the Wochenblatt des Architektenvereins zu Berlin; in 1892 the editor of the Deutsche Bauzeitung, the journal's later name, attributed the article to Meydenbauer. In the summer of 1867 Meydenbauer also demonstrated the method for surveying terrain and buildings in and around Freyburg (Unstrut), with sponsorship from the Prussian ministries of commerce and defense.[3]

Analytical photogrammetry and bundle adjustment

Electronic computers able to solve large least-squares problems became available in the late 1950s. Triggs and colleagues credit the basic photogrammetric bundle method to Duane C. Brown and his co-workers, who developed it for the U.S. Air Force in 1957-1959. Its first use was aerial cartography, and by the late 1960s bundle methods were also used for close-range measurement.[4]

Computer vision and structure from motion

Writing in 2000 for computer vision researchers, Triggs, McLauchlan, Hartley and Fitzgibbon noted that most results on bundle adjustment "appeared long ago in the photogrammetry and geodesy literatures" but were being reinvented in vision.[4] Automated pipelines followed. The 2006 Photo Tourism system from the University of Washington and Microsoft Research computed camera viewpoints and sparse 3D models from large photo collections, and Microsoft's Photosynth was based on that research.[13][14] The "Building Rome in a Day" work scaled reconstruction to city-sized image collections gathered from Internet photo-sharing sites.[15]

In the geosciences, Westoby and colleagues described structure from motion (SfM) in 2012 as a low-cost technique that works on "the same basic tenets as stereoscopic photogrammetry" but solves scene geometry and camera positions and orientations automatically, without a network of targets with known 3D positions.[5] In a 2019 editorial for The Photogrammetric Record, its editor Stuart Granshaw criticized the view, common in some SfM application papers, that photogrammetry is a new discipline.[12]

Types of photogrammetry

Photogrammetry is usually divided into aerial and close range.[4] In aerial photogrammetry the camera is carried by an aircraft, and the scene is usually shallow compared to the viewing height.[4] Automated aerial and close-range digital photogrammetry are widely used for three-dimensional topographic modelling, and such data can be used to create a digital elevation model (DEM), digital terrain model (DTM), or digital surface model (DSM) of a terrain's surface.[5][16]

Close-range photogrammetry typically uses hand-held or tripod-mounted cameras to measure and model complex architectural and engineering structures, set pieces, forensic scenes, human figures and similar subjects. Valve's 2015 VR guide, for example, describes hand-held and tripod capture of rooms and outdoor scenes with a digital SLR.[6]

How photogrammetry works

Camera geometry

To reconstruct the position and shape of objects from imagery, the geometry of the camera at the time of exposure must be known. For a frame camera this geometry is treated as a perspective (central) projection with the optical center of the camera as the perspective center. The collinearity condition states that the vector from the perspective center to a point on the image is a scaled version of the vector from the perspective center to the corresponding object point; the collinearity equations derived from it link image coordinates to object coordinates and have been extended to push-broom, three-line and panoramic scanners.[17] The lens must also be calibrated; Valve's guide notes that very wide-angle lenses can be hard to calibrate because of distortion in the image corners.[6]

Matching, alignment and bundle adjustment

The use of different angles allows for similarity-based mathematical analysis to be performed to reconstruct real-life objects and structures into a faithfully reproduced 3D space. The fact that each point has to be matched with the same point shot from a different angle means that the method is limited to static scenes containing opaque, non-specular surfaces; reflective, shiny and featureless surfaces tend to break down, while old buildings, ruins, tree trunks, rocky landscapes and broken concrete scan well.[6][18]

The COLMAP documentation describes structure from motion as three stages: feature detection and extraction, feature matching and geometric verification, and structure and motion reconstruction. The output is a 3D reconstruction together with the intrinsic and extrinsic parameters of every camera.[19] Westoby and colleagues describe the Scale Invariant Feature Transform (SIFT) used in the SfM methods popularized by Noah Snavely, which finds image features that are invariant to scaling and rotation and partly invariant to changes in lighting and viewpoint.[5] The camera positions and object coordinates are then refined together by bundle adjustment, which Triggs and colleagues define as "the problem of refining a visual reconstruction to produce jointly optimal 3D structure and viewing parameter (camera pose and/or calibration) estimates".[4] This stitching process works like a generation of panoramic photographs from multiple images, except that the camera must not stay in one place.[6]

A structure-from-motion reconstruction has no inherent scale or orientation. Its points sit in a relative "image-space" coordinate system and must be transformed to real-world coordinates, usually with a 3D similarity transform based on a small number of ground control points with known positions.[5]

Dense reconstruction, meshing and texturing

Once the cameras are aligned, the resulting sparse point cloud is followed by a dense one. In COLMAP's description, multi-view stereo (MVS) takes the output of SfM and computes depth and/or normal information for every pixel in an image (depth maps); fusing these maps from many images produces a dense point cloud of the scene.[19] The dense point cloud is used for generating a mesh composed of millions of triangles, and high-resolution textures are then created and applied to the generated mesh.[6] Surface reconstruction algorithms such as screened Poisson reconstruction recover the geometry, meshes can be simplified by quadric error metric decimation, and multi-view texture mapping assigns each face to the best camera image.[19]

The final scene can be rendered in a common 3D engine such as Unity. Such a scene can be enhanced with lighting that roughly matches the real lighting of the captured place, ambient audio, particle effects, or moving objects such as clouds in the background.[20]

Capture practice

Photographs for photogrammetry should be sharp and as free from sensor noise as possible. Valve recommends a deep depth of field (f/11 as a compromise with sharpness), a low ISO, plenty of overlap between neighbouring photos, and a couple of hundred photos for a detailed scene; image masks can remove unwanted content such as passing cars or pedestrians.[6][18] The COLMAP tutorial gives similar guidance: use images with good texture, similar lighting and high visual overlap, avoid texture-less surfaces and specular highlights, and do not take images from one spot while only rotating the camera.[19]

Photogrammetric processing software

Photogrammetric processing software ranges from stand-alone solutions to cloud-based platforms that are able to reduce otherwise very high hardware requirements for efficient processing.

Software Developer Status (October 2026)
Agisoft PhotoScan / Agisoft Metashape Agisoft PhotoScan renamed Metashape with version 1.5.0[21]
RealityCapture / RealityScan Capturing Reality (Epic Games) RealityCapture renamed RealityScan with version 2.0 in June 2025; version 2.2 released June 2026[22]
3DF Zephyr 3Dflow Free, Lite and full editions sold[16]
Autodesk 123D Catch Autodesk Support ended 31 March 2017[23]
Autodesk Memento / ReMake Autodesk Discontinued 1 December 2017; replaced by ReCap Photo[24]
Pix4D Pix4D PIX4Dmatic, PIX4Dmapper and other products[25]
Meshroom / AliceVision AliceVision Open source[26]
COLMAP Johannes L. Schönberger and contributors Free and open source[27]
EnsoMOSAIC 3D MosaicMill Aerial mapping software[28]

Agisoft PhotoScan and Metashape

Agisoft PhotoScan is developed by Agisoft, which Valve described in 2015 as a small Russian company in Saint Petersburg. It has a background in GIS, with many features aimed at surveyors and aerial photography, and it gained a following among computer graphics artists; it was the package the author of Valve's guide had the most experience with.[6] With version 1.5.0 the product was renamed Agisoft Metashape, the successor to PhotoScan, and the update was free for PhotoScan license owners of the same edition.[21] Agisoft describes Metashape as "a stand-alone software product that performs photogrammetric processing of digital images and generates 3D spatial data".[29]

The software comes in two different editions: Standard and Professional. Both editions support photogrammetric triangulation, generation and editing of a dense point cloud, generation and texturing of the 3D model, spherical panorama stitching, frame, fisheye, spherical and cylindrical cameras, and batch processing. The Professional Edition additionally comes with georeferenced DSM and DTM generation, ground control point support, multi-camera systems, measurements, dense point cloud classification, DEM editing, Python scripting, network and cloud processing, and 4D modeling for dynamic scenes, among other features.[30] Valve's guide describes a full high dynamic range workflow in PhotoScan, from bracketed shots merged to OpenEXR to OpenEXR texture export, and a cleanup technique in which a cleaner mesh modeled by hand is reimported and the photos are reprojected onto its UVs.[18]

RealityCapture and RealityScan

RealityCapture was developed by Capturing Reality. Epic Games announced its acquisition of the company in March 2021, and prices were cut for all customers; Quixel's Teddy Bergsman said at the time that RealityCapture had been "integral to the creation of Quixel Megascans since its inception".[31] Epic made the software free to artists and studios with revenue under US$1 million a year in 2024, and in June 2025 released RealityScan 2.0, the renamed RealityCapture, to unify its name with Epic's free RealityScan mobile app. RealityScan 2.0 required Windows and a CUDA-capable NVIDIA GPU and added AI-based masking, aerial lidar support and quality-analysis heatmaps; larger studios pay US$1,250 per seat per year.[32][22] Version 2.1 (November 2025) added a Linux command-line build for servers, SLAM data import and COLMAP project import,[33] and version 2.2 (June 2026) added GPU acceleration on AMD graphics cards under Windows, with the same licensing terms.[34] A 2025 study presented at the IEEE Gaming, Entertainment and Media conference evaluated RealityCapture for VR game development with Unreal Engine; it found that the tool cut development time while keeping geometric precision, although test users slightly preferred hand-made models for small objects they could pick up and handle.[35]

3DF Zephyr

3DF Zephyr is developed by 3Dflow of Verona, Italy, which describes it as software to "automatically reconstruct 3D models from photos". It can also combine photogrammetry with laser scans and produce orthophotos, DTMs and DSMs. As of October 2026 it is sold in three editions: a free edition limited to 50 photos, 3DF Zephyr Lite at 199 euros plus VAT (a perpetual license with 12 months of updates, which can also export videos), and the full 3DF Zephyr at 4,200 euros plus VAT as a perpetual license or 250 euros plus VAT per month. Earlier editions named Pro and Aerial are no longer listed.[16]

Autodesk 123D Catch

Autodesk 123D Catch was compatible with Windows Phone devices, iPhone, iPad, Android, or any regular camera. It let users create scans of real-life objects and turn these scans into realistic 3D models that could be viewed or shared with other people. Valve described it in 2015 as a cloud-based alternative to PhotoScan.[6] Autodesk ended support for 123D Catch, together with its other 123D apps, on 31 March 2017, and folded the tool into ReMake.[23]

Autodesk Memento

Autodesk Memento was an end-to-end solution for converting captured reality input (photos or scans) into high definition 3D meshes that could be cleaned up, fixed, and optimized for the Web, mobile or 3D printing. It was later renamed ReMake.[23] Autodesk stopped selling ReMake subscriptions on 7 September 2017 and discontinued the application on 1 December 2017, moving its functions into ReCap Pro as ReCap Photo.[24]

Pix4D

Pix4D is image processing software for mapping and modeling, capable of converting thousands of images into georeferenced 2D mosaics and 3D models. The Lausanne-based company's current products include PIX4Dmatic, PIX4Dmapper, PIX4Dcloud and PIX4Dcatch.[25]

Meshroom and COLMAP

Meshroom is the open-source application built on the AliceVision computer vision framework. Its node-graph pipeline reconstructs 3D scenes from still images, and it also includes pipelines for HDR fusion, panorama stitching and camera tracking.[26] COLMAP is "a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface", released as free and open-source software.[27] Its incremental SfM method was published by Johannes Schönberger and Jan-Michael Frahm at CVPR 2016.[36] The original NeRF and 3D Gaussian splatting papers both used COLMAP or its SfM method to calibrate their input cameras (see below).

EnsoMOSAIC 3D

EnsoMOSAIC 3D is a digital photogrammetric software for stereoscopic 3-dimensional data acquisition from EnsoMOSAIC aerial images and for processing large XYZ point clouds. EnsoMOSAIC itself is MosaicMill's software for aerial triangulation and orthomosaicking; version 7.5, released in 2013, added a data transfer to Espa 3D (EnsoMOSAIC 3D) so that camera self-calibration could be used for elevation points and terrain models.[28]

Mobile capture apps

Photogrammetry is also available on phones. Epic's RealityScan Mobile (version 1.7 as of June 2025) runs on Android and iOS and is free, including for commercial use.[32] Polycam offers photogrammetry and LiDAR capture, along with Gaussian splat tools.[37]

Applications in VR and AR

Scanned environments in VR

Valve's 2015 guide called photogrammetry results in room-scale VR "both compelling and unsettling" and described captured scenes available in the SteamVR demos as "Iceland Cinder Cone" and "Valve Demo Room".[6][20] For rendering in Unity, the guide advised limiting textures to 8192 by 8192 pixels, using unlit textured shaders and turning off shadow casting. According to Valve, a 1.5 million triangle mesh from PhotoScan with twelve 8K DXT1-compressed textures should run fine on a typical VR-oriented system.[20]

Valve's free collection of HTC Vive demos, The Lab (released 5 April 2016), began with a "postcard" of Vesper Peak in Washington, recreated using photogrammetry, where players could walk around, throw a stick for Fetch-bot and teleport between vantage points; further postcard scenes were included.[7] In June 2016 Valve released Destinations, a free Source 2 based tool for creating, sharing and visiting photogrammetry scenes on the HTC Vive and Oculus Rift; Valve's Adam Foster built its "English Church" scene from about 450 DSLR photographs.[38]

Independent developers took the same approach. The Realities app by realities.io, a free Steam release dated 5 April 2016,[39] used a photogrammetry pipeline to rebuild real places that users could walk through; Road to VR's Ben Lang wrote that, for the most part, his demo scene was "more detailed and realistic looking than any other virtual world" he had been inside, and the developers joked that photogrammetry let them "outsource the lightning engine to the real world".[40] The same studio later made Puzzling Places, a VR puzzle game that combines disassembled photogrammetry 3D scans of real places with soundscapes.[41]

Game assets

Games also use photogrammetry for props and terrain. In a March 2014 post about The Vanishing of Ethan Carter, The Astronauts described using Agisoft PhotoScan to turn photographs into assets, from a boulder captured in 26 photos to a quarry wall section captured in 242, and noted that a raw scanned object usually weighed between 2 and 20 million triangles, so assets had to be optimized and retopologized for the game.[42] The studio later released a separate VR edition, The Vanishing of Ethan Carter VR, for the Oculus Rift and HTC Vive on Steam.[43] Quixel's Teddy Bergsman has said that RealityCapture was integral to the creation of the Megascans library of scanned assets.[31]

Microsoft's Flight Simulator series uses photogrammetry for 3D city models. World Update 20 (September 2025), built with Bing Maps, Gaya and Asobo for both Microsoft Flight Simulator (2020) and Microsoft Flight Simulator 2024, added 23 areas of interest in Japan recreated with updated photogrammetry, including 16 in Tokyo.[44] The 2020 game, Microsoft Flight Simulator, received VR support with Sim Update 2, scheduled by Microsoft for 22 December 2020.[45]

AR content creation

At WWDC 2021 Apple introduced Object Capture, a photogrammetry API on macOS Monterey that is part of RealityKit 2. Developers photograph an object from all angles with an iPhone, iPad, DSLR or drone, start a photogrammetry session on a Mac and generate a model; the output includes a geometric mesh and material maps.[8][46] The resulting USDZ files are optimized for AR Quick Look and can be added to AR scenes in Reality Composer; Apple said developers including Wayfair and Etsy were using it to create 3D models of real-world products.[46]

Volumetric video

Multi-camera photogrammetric capture of moving subjects underlies volumetric video. Collet and colleagues at Microsoft, citing interest driven by consumer VR and AR hardware such as HoloLens, described in 2015 a greenscreen stage whose default setup used 53 RGB and 53 infrared synchronized cameras around a capture volume 2.8 m in diameter and 2.5 m high, recording 2048 by 2048 images at 30 Hz. Their pipeline fused RGB, infrared and silhouette information in a multimodal multi-view stereo step, generated dynamic textured surfaces, tracked the meshes over time and encoded them as an MPEG video stream.[11]

Inspection and engineering in VR

Researchers have also built VR tools around photogrammetric models. PhotoTwinVR, described in 2019 by Tadeja and colleagues, is a gesture-controlled system for manipulating, inspecting and measuring 3D photogrammetric models in VR; in an observational study, three domain experts used it to survey a model of an existing pipe rack.[47]

Relation to neural radiance fields and Gaussian splatting

Radiance field methods reuse the front end of the photogrammetric pipeline but replace the textured mesh with a different scene representation. The original NeRF paper (2020) trains a fully connected network that outputs volume density and view-dependent emitted radiance; its only required input is a set of images with known camera poses, which the authors estimated for real scenes with the COLMAP structure-from-motion package.[9]

3D Gaussian splatting (Kerbl and colleagues, 2023) starts from the same input: cameras calibrated with SfM. It initializes a set of 3D Gaussians from the sparse point cloud that SfM produces, and reaches real-time novel-view synthesis of at least 30 frames per second at 1080p resolution.[10] Consumer VR capture has adopted the technique. Meta's Horizon Hyperscape Capture, rolled out in beta in the US in September 2025 for the Meta Quest 3 and Meta Quest 3S, has users scan a room with the headset; Meta's servers process the scan in 1 to 8 hours and stream the resulting Gaussian splat scene back to the headset.[48]

See also

References

  1. ↑ "What Is ASPRS?". American Society for Photogrammetry and Remote Sensing. https://www.asprs.org/Main/Main/About/What-Is-ASPRS-.aspx. Retrieved 2026-10-06.
  2. ↑ 2.0 2.1 2.2 L. Polidori (2020). "On Laussedat's contribution to the emergence of photogrammetry". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLIII-B2-2020, pp. 893-899. doi:10.5194/isprs-archives-XLIII-B2-2020-893-2020. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-893-2020. Retrieved 2026-10-06.
  3. ↑ 3.0 3.1 3.2 Albrecht Grimm (2007). "The Origin of the Term Photogrammetry". Photogrammetric Week '07 (ed. Dieter Fritsch), pp. 53-60. Wichmann Verlag. https://phowo.ifp.uni-stuttgart.de/publications/phowo07/080Grimm.pdf. Retrieved 2026-10-06.
  4. ↑ 4.0 4.1 4.2 4.3 4.4 4.5 Bill Triggs, Philip F. McLauchlan, Richard I. Hartley, Andrew W. Fitzgibbon (2000). "Bundle Adjustment - A Modern Synthesis". Vision Algorithms: Theory and Practice, Lecture Notes in Computer Science, vol. 1883, pp. 298-372. Springer. doi:10.1007/3-540-44480-7_21. https://doi.org/10.1007/3-540-44480-7_21. Retrieved 2026-10-06.
  5. ↑ 5.0 5.1 5.2 5.3 5.4 5.5 M.J. Westoby, J. Brasington, N.F. Glasser, M.J. Hambrey, J.M. Reynolds (2012). "'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications". Geomorphology, vol. 179, pp. 300-314. doi:10.1016/j.geomorph.2012.08.021. https://doi.org/10.1016/j.geomorph.2012.08.021. Retrieved 2026-10-06.
  6. ↑ 6.00 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 6.10 6.11 "Photogrammetry in VR - part 1 of 3". SteamVR announcements, Steam. Valve. 2015-09-21. https://steamcommunity.com/games/250820/announcements/detail/117448248511524033. Retrieved 2026-10-06.
  7. ↑ 7.0 7.1 Jason Evangelho (2016-04-06). "The Lab Review - The Most Polished Collection of Free Vive VR Demos". UploadVR. https://www.uploadvr.com/the-lab-vive-vr-review/. Retrieved 2026-10-06.
  8. ↑ 8.0 8.1 "Create 3D models with Object Capture (WWDC21)". Apple Developer. Apple. 2021-06. https://developer.apple.com/videos/play/wwdc2021/10076/. Retrieved 2026-10-06.
  9. ↑ 9.0 9.1 Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng (2020). "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis". Computer Vision - ECCV 2020, Lecture Notes in Computer Science, pp. 405-421. Springer. doi:10.1007/978-3-030-58452-8_24. https://doi.org/10.1007/978-3-030-58452-8_24. Retrieved 2026-10-06.
  10. ↑ 10.0 10.1 Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuehler, George Drettakis (2023). "3D Gaussian Splatting for Real-Time Radiance Field Rendering". ACM Transactions on Graphics, vol. 42, no. 4. doi:10.1145/3592433. https://doi.org/10.1145/3592433. Retrieved 2026-10-06.
  11. ↑ 11.0 11.1 Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, Steve Sullivan (2015). "High-quality streamable free-viewpoint video". ACM Transactions on Graphics, vol. 34, no. 4. doi:10.1145/2766945. https://doi.org/10.1145/2766945. Retrieved 2026-10-06.
  12. ↑ 12.0 12.1 Stuart I. Granshaw (2019). "Laussedat bicentenary: origins of photogrammetry". The Photogrammetric Record, vol. 34, no. 166, pp. 128-147. doi:10.1111/phor.12277. https://doi.org/10.1111/phor.12277. Retrieved 2026-10-06.
  13. ↑ "Photo Tourism: Exploring Photo Collections in 3D". University of Washington Graphics and Imaging Laboratory. https://phototour.cs.washington.edu/. Retrieved 2026-10-06.
  14. ↑ Noah Snavely, Steven M. Seitz, Richard Szeliski (2006). "Photo tourism: exploring photo collections in 3D". ACM SIGGRAPH 2006 Papers, p. 835. doi:10.1145/1179352.1141964. https://doi.org/10.1145/1179352.1141964. Retrieved 2026-10-06.
  15. ↑ Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M. Seitz, Richard Szeliski (2011). "Building Rome in a day". Communications of the ACM, vol. 54, no. 10, pp. 105-112. doi:10.1145/2001269.2001293. https://doi.org/10.1145/2001269.2001293. Retrieved 2026-10-06.
  16. ↑ 16.0 16.1 16.2 "3DF Zephyr - photogrammetry software". 3Dflow. https://www.3dflow.net/3df-zephyr-photogrammetry-software/. Retrieved 2026-10-06.
  17. ↑ Ayman Habib, Andinet Asmamaw, Devin Kelley, Manja May (2000-01). "Linear Features in Photogrammetry". Geodetic Science and Surveying Report No. 450. The Ohio State University. https://earthsciences.osu.edu/sites/earthsciences.osu.edu/files/report-450.pdf. Retrieved 2026-10-06.
  18. ↑ 18.0 18.1 18.2 "Photogrammetry in VR - part 2 of 3". SteamVR announcements, Steam. Valve. 2015-09-21. https://steamcommunity.com/games/250820/announcements/detail/117448248511523471. Retrieved 2026-10-06.
  19. ↑ 19.0 19.1 19.2 19.3 "Tutorial". COLMAP documentation. https://colmap.github.io/tutorial.html. Retrieved 2026-10-06.
  20. ↑ 20.0 20.1 20.2 "Photogrammetry in VR - part 3 of 3". SteamVR announcements, Steam. Valve. 2015-09-21. https://steamcommunity.com/games/250820/announcements/detail/117448248511522945. Retrieved 2026-10-06.
  21. ↑ 21.0 21.1 "How can I update from PhotoScan to Metashape, is it free?". Agisoft Helpdesk. Agisoft. https://agisoft.freshdesk.com/support/solutions/articles/31000147966-how-can-i-update-from-photoscan-to-metashape-is-it-free-. Retrieved 2026-10-06.
  22. ↑ 22.0 22.1 "RealityScan 2.0: New release brings powerful new features to a rebranded RealityCapture". RealityScan. Epic Games. 2025-06-17. https://www.realityscan.com/news/realityscan-20-new-release-brings-powerful-new-features-to-a-rebranded-realitycapture. Retrieved 2026-10-06.
  23. ↑ 23.0 23.1 23.2 Jim Thacker (2016-12-23). "Autodesk cans its 123D apps". CG Channel. https://www.cgchannel.com/2016/12/autodesk-cans-its-123d-apps/. Retrieved 2026-10-06.
  24. ↑ 24.0 24.1 Nick Lievendag (2017-10-02). "Autodesk Discontinues (Free) Photogrammetry Solution ReMake". 3D Mag. https://www.3dmag.com/news/autodesk-remake-discontinued-becomes-recap-photo-alternatives/. Retrieved 2026-10-06.
  25. ↑ 25.0 25.1 "About us". Pix4D. https://www.pix4d.com/about-us/. Retrieved 2026-10-06.
  26. ↑ 26.0 26.1 Carsten Griwodz, Simone Gasparini, Lilian Calvet, Pierre Gurdjos, Fabien Castan, Benoit Maujean, Gregoire De Lillo, Yann Lanthony (2021). "AliceVision Meshroom: An open-source 3D reconstruction pipeline". Proceedings of the 12th ACM Multimedia Systems Conference (MMSys '21), pp. 241-247. doi:10.1145/3458305.3478443. https://doi.org/10.1145/3458305.3478443. Retrieved 2026-10-06.
  27. ↑ 27.0 27.1 "COLMAP". COLMAP documentation. https://colmap.github.io/. Retrieved 2026-10-06.
  28. ↑ 28.0 28.1 "EnsoMosaic v7.5 released". MundoGEO. 2013-05-28. https://mundogeo.com/en/2013/05/28/ensomosaic-v7-5-released/. Retrieved 2026-10-06.
  29. ↑ "Agisoft Metashape". Agisoft. https://www.agisoft.com/. Retrieved 2026-10-06.
  30. ↑ "Compare: Standard vs Professional edition". Agisoft. https://www.agisoft.com/features/compare/. Retrieved 2026-10-06.
  31. ↑ 31.0 31.1 Chris Wallace (2021-03-10). "Epic Games acquires Capturing Reality". MCV/DEVELOP. https://mcvuk.com/business-news/epic-games-acquires-capturing-reality/. Retrieved 2026-10-06.
  32. ↑ 32.0 32.1 Jim Thacker (2025-06-18). "Epic Games releases RealityScan 2.0 and RealityScan Mobile 1.7". CG Channel. https://www.cgchannel.com/2025/06/epic-games-releases-realityscan-2-0-and-realityscan-mobile-1-7/. Retrieved 2026-10-06.
  33. ↑ Jim Thacker (2025-11-26). "Epic Games releases RealityScan 2.1". CG Channel. https://www.cgchannel.com/2025/11/epic-games-releases-realityscan-2-1/. Retrieved 2026-10-06.
  34. ↑ Jim Thacker (2026-06-25). "Epic Games releases RealityScan 2.2 with AMD GPU support". CG Channel. https://www.cgchannel.com/2026/06/epic-games-releases-realityscan-2-2-with-amd-gpu-support/. Retrieved 2026-10-06.
  35. ↑ Santiago Berrezueta-Guzman, Andrei Koshelev, Stefan Wagner (2025). "From Reality to Virtual Worlds: The Role of Photogrammetry in Game Development". IEEE Gaming, Entertainment and Media Conference (GEM) 2025, arXiv:2505.16951. doi:10.1109/GEM66882.2025.11155764. https://arxiv.org/abs/2505.16951. Retrieved 2026-10-06.
  36. ↑ Johannes L. Schönberger, Jan-Michael Frahm (2016). "Structure-from-Motion Revisited". 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4104-4113. doi:10.1109/CVPR.2016.445. https://doi.org/10.1109/CVPR.2016.445. Retrieved 2026-10-06.
  37. ↑ "Polycam". Polycam. https://poly.cam/. Retrieved 2026-10-06.
  38. ↑ Scott Hayden (2016-06-09). "Valve Releases 'Destinations', a Free VR Creation Tool Built on Source 2 Engine". Road to VR. https://www.roadtovr.com/valve-releases-destinations-a-free-vr-creation-tool/. Retrieved 2026-10-06.
  39. ↑ "Realities". Steam. Valve. https://store.steampowered.com/app/452710/Realities/. Retrieved 2026-10-06.
  40. ↑ Ben Lang (2016-03-11). "Inside 'Realities' Jaw-droppingly Detailed Photogrammetric VR Environments". Road to VR. https://www.roadtovr.com/realities-photogrammetry-virtual-reality-htc-vive/. Retrieved 2026-10-06.
  41. ↑ "Building Puzzling Places: How the Realities.io Team Brought Their World-Building App to Life". Meta for Developers blog. Meta. 2021-09-02. https://developers.meta.com/vr/blog/building-puzzling-places-how-the-realitiesio-team-brought-their-world-building-app-to-life/. Retrieved 2026-10-06.
  42. ↑ Andrzej Poznanski (2014-03-25). "Visual Revolution of The Vanishing of Ethan Carter". The Astronauts. http://www.theastronauts.com/2014/03/visual-revolution-vanishing-ethan-carter/. Retrieved 2026-10-06.
  43. ↑ "The Vanishing of Ethan Carter VR". Steam. Valve. https://store.steampowered.com/app/457880/. Retrieved 2026-10-06.
  44. ↑ "Microsoft Flight Simulator Releases World Update 20: Japan". Microsoft Flight Simulator. 2025-09-25. https://www.flightsimulator.com/world-update-20/. Retrieved 2026-10-06.
  45. ↑ "December 17th, 2020 Development Update". Microsoft Flight Simulator. 2020-12-17. https://www.flightsimulator.com/december-17th-2020-development-update/. Retrieved 2026-10-06.
  46. ↑ 46.0 46.1 Sarah Perez (2021-06-08). "Apple's RealityKit 2 allows developers to create 3D models for AR using iPhone photos". TechCrunch. https://techcrunch.com/2021/06/08/apples-realitykit-2-allows-developers-to-create-3d-models-for-ar-using-iphone-photos/. Retrieved 2026-10-06.
  47. ↑ Slawomir Konrad Tadeja, Wojciech Rydlewicz, Yupu Lu, Per Ola Kristensson, Tomasz Bubas, Maciej Rydlewicz (2019-11-22). "PhotoTwinVR: An Immersive System for Manipulation, Inspection and Dimension Measurements of the 3D Photogrammetric Models of Real-Life Structures in Virtual Reality". arXiv:1911.09958. https://arxiv.org/abs/1911.09958. Retrieved 2026-10-06.
  48. ↑ David Heaney (2025-09-17). "Hands-On: Meta Horizon Hyperscape Captures Photorealistic VR Scenes On Quest 3". UploadVR. https://www.uploadvr.com/meta-horizon-hyperscape-photorealistic-scene-capture-quest-3/. Retrieved 2026-10-06.
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