Enhanced 3-D Urban Scene Reconstruction and Point Cloud Densification Using Gaussian Splatting and Google Earth Imagery
Bibliographic record
Abstract
Three-dimensional urban scene reconstruction and modeling is a crucial research area. From a technical perspective, it is an interdisciplinary research area spanning computer vision, computer graphics, and photogrammetry. Its applications span across multiple disciplines including autonomous navigation with 3-D scene understanding, remote sensing/photogrammetry for the creation of 3-D maps from aerial/drone/satellite images, geographic information systems with urban digital twins, augmented and virtual reality with photorealistic scene reconstructions. Using Google Earth imagery, we create a 3-D Gaussian splatting (3DGS) model of the Waterloo region centered on the University of Waterloo, and are able to achieve view-synthesis results far exceeding previous 3-D view-synthesis results based on neural radiance fields (NeRFs)which we demonstrate in our benchmark. We also retrieve the 3-D geometry of the scene using the 3-D point cloud extracted from the 3DGS model, thereby reconstructing both the 3-D geometry and photorealistic lighting of the large-scale urban scene.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".