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Record W2004787689 · doi:10.1145/2814940.2814956

Flying Frustum

2015· article· en· W2004787689 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Calgary
FundersCMG Reservoir Simulation Foundation
KeywordsFrustumSituatedComputer scienceTerrainHuman–computer interactionHeadsetInterface (matter)Situation awarenessComputer graphics (images)Artificial intelligenceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

We present Flying Frustum, a 3D spatial interface that enables control of semi-autonomous UAVs (Unmanned Aerial Vehicles) using pen interaction on a physical model of the terrain, and that spatially situates the information streaming from the UAVs onto the physical model. Our interface is based on a 3D printout of the terrain, which allows the operator to enter goals and paths to the UAV by drawing them directly on the physical model. In turn, the UAV's streaming reconnaissance information is superimposed on the 3D printout as a view frustum, which is situated according to the UAV's position and orientation on the actual terrain. We argue that Flying Frustum's 3D spatially situated interaction can potentially help improve human-UAV awareness and enhance the overall situational awareness. We motivate our design approach for Flying Frustum, discuss previous related work in CSCW and HRI, present our preliminary prototype using both handheld and headset augmented reality interfaces, reflect on Flying Frustum's strengths and weaknesses, and discuss our plans for future evaluation and prototype improvements.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.080
GPT teacher head0.288
Teacher spread0.208 · 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

Quick stats

Citations18
Published2015
Admission routes2
Has abstractyes

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