Deep Learning Anthropomorphic 3D Point Clouds from a Single Depth Map Camera Viewpoint
Bibliographic record
Abstract
In footwear, fit is highly dependent on foot shape, which is not fully captured by shoe size. Scanners can be used to acquire better sizing information and allow for more personalized footwear matching, however when scanning an object, many images are usually needed for reconstruction. Semantics such as knowing the kind of object in view can be leveraged to determine the full 3D shape given only one input view. Deep learning methods have been shown to be able to reconstruct 3D shape from limited inputs in highly symmetrical objects such as furniture and vehicles. We apply a deep learning approach to the domain of foot scanning, and present a method to reconstruct a 3D point cloud from a single input depth map. Anthropomorphic body parts can be challenging due to their irregular shapes, difficulty for parameterizing and limited symmetries. We train a view synthesis based network and show that our method can produce foot scans with accuracies of 1.55 mm from a single input depth map.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".