Meta learning for point cloud analysis
Bibliographic record
Abstract
Point cloud has been highly attracting the attention of the research community, due to their numerous applications in 3D computer vision. While learning-based approaches for point cloud problems have achieved impressive progress, generalization to unknown testing environments remains a major challenge due to the large discrepancies of data captured by different 3D sensors. Existing methods typically train a generic model and the same trained model is applied on each test instance. This could be sub-optimal since it is difficult for the same model to handle all the variations during testing. In this thesis, we propose novel frameworks for point cloud problems that adapt the model in an instance-specific manner during inference. Our model is trained using a meta-learning scheme to provide the model with the ability of fast and effective adaptation at test time. First, we consider the problem of point cloud registration. The objective is to estimate the 3D transformation that aligns a pair of partially overlapped point clouds. Next, we investigate the point cloud upsampling problem. In this setting, the goal is to generate high-resolution point clouds from sparse point clouds. Experimental results demonstrate the effectiveness of our proposed frameworks in improving the performance of state-of-the-art models and achieving superior results.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".