Fine Feature Reconstruction in Point Clouds by Adversarial Domain Translation
Bibliographic record
Abstract
Point cloud neighborhoods are unstructured and often lacking in fine details, particularly when the original surface is sparsely sampled. This has motivated the development of methods for reconstructing these fine geometric features before the point cloud is converted into a mesh, usually by some form of upsampling of the point cloud. We present a novel data-driven approach to reconstructing fine details of the underlying surfaces of point clouds at the local neighborhood level, along with normals and locations of edges. This is achieved by an innovative application of recent advances in domain translation using GANs. We "translate" local neighborhoods between two domains: point cloud neighborhoods and triangular mesh neighborhoods. This allows us to obtain some of the benefits of meshes at training time, while still dealing with point clouds at the time of evaluation. By resampling the translated neighborhood, we can obtain a denser point cloud equipped with normals that allows the underlying surface to be easily reconstructed as a mesh. Our reconstructed meshes preserve fine details of the original surface better than the state of the art in point cloud upsampling techniques, even at different input resolutions. In addition, the trained GAN can generalize to operate on low resolution point clouds even without being explicitly trained on low-resolution data. We also give an example demonstrating that the same domain translation approach we use for reconstructing local neighborhood geometry can also be used to estimate a scalar field at the newly generated points, thus reducing the need for expensive recomputation of the scalar field on the dense point cloud.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".