3DGS-to-PC: 3D Gaussian Splatting to Dense Point Clouds
Bibliographic record
Abstract
3D Gaussian Splatting (3DGS) excels at producing highly detailed 3D reconstructions, but these scenes often require specialised renderers for effective visualisation. In contrast, point clouds are a widely used 3D representation and are compatible with most popular 3D processing soft-ware packages. In this work, we introduce 3DGS-to-PC, a highly customisable framework for transforming 3DGS scenes into dense, accurate point clouds. We first render new Gaussian colours by calculating the visual contributions that each Gaussian made to each pixel, and then as-signing these Gaussians with the colour of the pixel they contributed most to. We then remove Gaussians that contribute little to the scene, reducing noise. Finally, we gen-erate new points by sampling from each Gaussian as a mul-tivariate normal distribution, while removing erroneously generated points. The number of points per Gaussian is determined based on its relative scale and pixel contributions. The result is a dense point cloud that accurately represents each scene. At time of writing, there is no ded-icated method for converting a 3DGS scene into a point cloud. We evaluate our method against 3DGS meshing techniques - from which points can be sampled - and show that our method is competitive with state-of-the-art approaches that require training of the scene, while performing more efficiently than standard meshing methods, such as Pois-son Surface Reconstruction. 3DGS-to-PC is efficient, typically taking under a minute to process complex scenes, thus providing an effective tool for converting 3DGS data into robust point clouds. Our codebase can be accessed via https://github.com/Lewis-Stuart-11/3DGS-to-PC.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".