Energy-based Body Reconstruction from Sparse Image Sequences using Single Depth Camera
Bibliographic record
Abstract
Commercial depth cameras enable personal 3D scanning for digital sizing and virtualization, which are applicable to a variety of imaging and virtual systems. We introduce an accessible and cost-effective solution using a single depth camera and minimal additional hardware. We present a point cloud fusion algorithm to reconstruct a 3D model of the human body from a sparse set of RGBD images. The challenges we address are threefold: (1) complex body geometry; (2) non-rigid motion; and (3) self-occlusions with little image overlap. Our system leverages a skeleton prior to track non-rigid body motion and a visual hull prior to promote spatial consistency. Our fusion algorithm is evaluated on our synthetic dataset featuring controlled body motion. We simulate joint estimation errors and demonstrate algorithm robustness to joint estimation. In comparison with existing techniques of various complexity, our algorithm uses a simpler setup with fewer input images while maintaining low reconstruction error, a crucial step towards accessible 3D scanning technology.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".