Leveraging Transformer and CNN for Monocular 3D Point Cloud Reconstruction
Bibliographic record
Abstract
A transformer-based 3D object reconstruction approach is proposed in this paper to process an input monocular RGB image. This is carried out by a network containing two branches: (i) the convolutional neural network (CNN) branch and (ii) the transformer branch. The CNN branch aims to capture the local features and tiny details of the input image and convert them into the thin structures of the 3D point cloud output. The transformer branch, on the other hand, attends to the global structures and features, capturing long-distance relationships in the input image and transforming them from the feature space to the point cloud space to construct the global geometry of the 3D output. The transformer branch enables the method to learn to attend to the most relevant image features for each 3D point in the output. Moreover, point clouds generated by a combination of the transformer and CNN maintain the general geometrical structure of the object while preserving fine-level features only where needed. This reduces the memory requirement, enabling more accurate results compared to existing methods without losing computational efficiency. We also design and implement different network architectures to determine the required elements in the proposed network. All the architectures are evaluated using a proper dataset, and the results are compared to existing methods. Simulations demonstrate the superior performance of the proposed approach1.
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 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 | 0.000 |
| 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".