DeepNRSfPP: Learning-Based Real-Time Non-Rigid Structure-from-Perspective Projection
Bibliographic record
Abstract
This paper, propose DeepNRSfPP, a novel hybrid learning-based framework for real-time non-rigid 3D reconstruction from monocular videos under perspective projection. Traditional non-rigid structure-from-motion (NRSfM) techniques often rely on orthographic assumptions and are computationally intensive, making them unsuitable for real-world, real-time applications. Our approach builds on the strengths of NRSfPP by integrating deep neural networks with geometric constraints, enabling fast and accurate reconstruction of dynamic, deformable surfaces from single-view video. Specifically, DeepNRSfPP leverages a deep temporal encoder to regress coarse 3D shapes and a perspective-aware optimization layer to refine reconstructions using motion smoothness and projection consistency. We evaluate our model on several benchmark datasets and demonstrate significant improvements over both classical and recent learning-based baselines in terms of accuracy and speed. Our system achieves real-time performance (>30 FPS) while maintaining fine-grained reconstruction quality, making it suitable for applications in augmented reality, human motion capture, and robotic perception.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".