Pruning-Based Efficient Point Generation Network for 3D Reconstruction of Single Images
Bibliographic record
Abstract
Single view reconstruction is a problem of 3D reconstruction given only a single 2D RGB image.Recently, an end-to-end learning framework has been implemented, resulting in a 3D point generation network.Despite the effectiveness of a 3D point generation network, there are needs for high storage and high computational cost during reconstruction.This paper proposes a new method of single view reconstruction using pruning and templatebased point generation network (PGN) given only a single RGB image as the input.The template, which is the encoded structure of the input image, used to guide the point generation process and helps maintain spatial consistency during reconstruction.We propose a 3D template-based PGN followed by network pruning that can reduce a significant number of resources while preserving the reconstruction performance.Experiments on the ShapeNet dataset achieved a 45% reduction of network parameters without sacrificing much Chamfer distance increment, i.e., 0.001238.This study shows that weight pruning on the image encoder layers can improve efficiency without reducing the effectiveness of a 3D point generation network.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".