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Interactive dense point clouds in a game engine

2020· article· en· W3015948001 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueISPRS Journal of Photogrammetry and Remote Sensing · 2020
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversité Laval
FundersEuropean Social FundBusiness FinlandAcademy of Finland
KeywordsComputer sciencePoint cloudGame engineRendering (computer graphics)VisualizationPoint (geometry)Human–computer interactionVideo game developmentVirtual realityComputer graphics (images)Game designArtificial intelligence

Abstract

fetched live from OpenAlex

With the development of 3D measurement systems, dense colored point clouds are increasingly available. However, up to now, their use in interactive applications has been restricted by the lack of support for point clouds in game engines. In addition, many of the existing applications for point clouds lack the capacity for fluent user interaction and application development. In this paper, we present the development and architecture of a game engine extension facilitating the interactive visualization of dense point clouds. The extension allows the development of game engine applications where users edit and interact with point clouds. To demonstrate the capabilities of the developed extension, a virtual reality head-mounted display is used and the rendering performance is evaluated. The result shows that the developed tools are sufficient for supporting real-time 3D visualization and interaction. Several promising use cases can be envisioned, including both the use of point clouds as 3D assets in interactive applications and leveraging the game engine point clouds in geomatics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.278
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it