3D Object Reconstruction Using Geometric Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fragmented objects are encountered in a variety of diverse engineering and scientific fields including industrial inspection, customized medical prosthesis design, forensic science, paleontology, and archaeology. The arbitrarily broken pieces must be reassembled and new material often added to complete the process of shape reconstruction. To prevent physical damage of the pieces during reconstruction and enhance shape visualization scientists have begun to exploit 3D data acquisition and graphical modeling tools. An algorithm for enabling free-form shape reconstruction from digitized data of fragmented pieces is described in this paper. The method exploits the topological structure and learning algorithm of a 3D self-organizing feature map (SOFM). The lattice of the SOFM is a spherical mesh that maintains the relative connectivity of the neighboring nodes as it transforms under external forces. The weight nodes of the lattice represent vertices of the constituent elements in the facetted surface model. The technique is illustrated by reconstructing two clay objects with closed geometries from several fragmented parts
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it