Design to Fabrication Workflow in Mixed Reality
Bibliographic record
Abstract
This technical showcase will present research toward the application of Extended Reality technology in the design and construction of physical architectural environments. We have been working with the use of interactive holographic instructions linked to parametric design models that can be viewed and edited by users wearing Head-Mounted Displays (HMD) in real time. We have also incorporated more consumer-accessible mobile devices in the form of phones and tablets that support mixed reality in our testing. The goal of this research is to demonstrate the capability of mixed reality to effectively and meaningfully assist in the production of physical construction at architectural scale. We have focused on a few applications of this that are independently useful and particularly significant when incorporated into a design – to – fabrication workflow. One design application is the ability instantiate, verify, and refine a design in a mixed reality setting. A second application is with regard to the fabrication of designed components, particularly when nonstandard or not modular, in the ability to transfer instructions through holographic projection to a component fabrication procedure therefore dramatically simplifying a component production process. A third application is with the construction or assembly of said components with the mixed reality environment able to register the location of components in physical space as well as include build instructions solely through the user interface of the head-mounted display. All three applications eliminate otherwise necessary external measuring devices and printed drawings in these phases of a design to construction workflow. Our technical showcase will present the design- to-construction workflow involved with a sculpture designed to be installed at the Autodesk Technology Centre in Toronto. The complete model and example build instructions will be presented in a WebXR supported interface to enable participants a similar experience to the actual extended reality workflow.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".