SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D\n Reconstruction
Bibliographic record
Abstract
Recent work has made significant progress in learning object meshes with weak\nsupervision. Soft Rasterization methods have achieved accurate 3D\nreconstruction from 2D images with viewpoint supervision only. In this work, we\nfurther reduce the labeling effort by allowing such 3D reconstruction methods\nleverage unlabeled images. In order to obtain the viewpoints for these\nunlabeled images, we propose to use a Siamese network that takes two images as\ninput and outputs whether they correspond to the same viewpoint. During\ntraining, we minimize the cross entropy loss to maximize the probability of\npredicting whether a pair of images belong to the same viewpoint or not. To get\nthe viewpoint of a new image, we compare it against different viewpoints\nobtained from the training samples and select the viewpoint with the highest\nmatching probability. We finally label the unlabeled images with the most\nconfident predicted viewpoint and train a deep network that has a\ndifferentiable rasterization layer. Our experiments show that even labeling\nonly two objects yields significant improvement in IoU for ShapeNet when\nleveraging unlabeled examples. Code is available at\nhttps://github.com/IssamLaradji/SSR.\n
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".