3SGAN: 3D Shape Embedded Generative Adversarial Networks
Bibliographic record
Abstract
Despite recent advances in Generative Adversarial Models(GAN) for image generation, significant gaps remain concerning the generation of boundary and spatial structure. In this paper, we propose a new approach to generate edge and depth information combined with an RGB image to solve this problem. More specifically, we propose two new regularization models. Our first model enforces image-depth-edge alignments by controlling the second-order derivative of depth and the first-order derivative of RGB maps, enforcing smoothness and consistency. The second model leverages multiview synthesis to regularize RGB and depth by computing the difference between an expected rotated object compared to a conditionally generated view of the object; enforcing projection consistency enables the model to directly learn spatial structures and depths. To evaluate our approach, we generated an RGB-D dataset with edge contours from ShapeNet models. Furthermore, we utilized an existing RGB-D dataset, NYU Depth V2 with edges learned by the Holistically-nested Edge Detection model.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".