Frequency-Based 3D Reconstruction of Transparent and Specular Objects
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
3D reconstruction of transparent and specular objects is a very challenging topic in computer vision. For transparent and specular objects, which have complex interior and exterior structures that can reflect and refract light in a complex fashion, it is difficult, if not impossible, to use either passive stereo or the traditional structured light methods to do the reconstruction. We propose a frequency-based 3D reconstruction method, which incorporates the frequency-based matting method. Similar to the structured light methods, a set of frequency-based patterns are projected onto the object, and a camera captures the scene. Each pixel of the captured image is analyzed along the time axis and the corresponding signal is transformed to the frequency-domain using the Discrete Fourier Transform. Since the frequency is only determined by the source that creates it, the frequency of the signal can uniquely identify the location of the pixel in the patterns. In this way, the correspondences between the pixels in the captured images and the points in the patterns can be acquired. Using a new labelling procedure, the surface of transparent and specular objects can be reconstructed with very encouraging results.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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