Ray Tracing Modelling Using LiDAR 3D Scans for Rapid Acoustical Measurement and Simulation
Bibliographic record
Abstract
Acoustics play a key role in the comfort, usability, and accessibility of a space. Traditional acoustical characterization methods are challenging to interpret, require specialized equipment, and do not account for spatial variations or modifications to a room. This study introduces a novel approach that combines ray tracing-based simulation with a smartphone LiDAR scanner or 3D models to rapidly characterize and simulate the acoustics of a space. Using the Carleton University tunnel system as a case study, 3D models generated from a low-cost portable smartphone LiDAR scan and an architectural model were compared against physical acoustical measurements. Reverberation times (RT30 and RT60), simulated audio, and spectrograms were used to evaluate the performance of the models. Results demonstrated that the architectural model provided high fidelity in both quantitative and qualitative measures, while the smartphone LiDAR scan offered a rapid and accessible, albeit less precise, alternative. The findings highlight the potential of combining ray tracing simulations with readily available technology to simulate and assess room acoustics rapidly and effectively.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".