Bibliographic record
Abstract
This thesis focuses on the development of a machine learning-based 3D computer vision system that can represent complex 3D shapes and scenes with a group of simple geometric primitives. The proposed system is a multi-stage process that utilizes a learning-based Hierarchical Gaussian Mixture Model (HGMM) as the shape representation, which is then taken as the input into the primitive detection module. The proposed computer vision system requires no supervision while training and is able to provide accurate and robust approximations of 3D shapes via a set of simple geometric primitives, such as cuboids, planes, or spheres. This significantly reduces the memory footprint while keeping a meaningful and discriminative representation of the original model. The primitive detection system pipeline involves two sequential stages. The first stage involves the training and post-processing of the HGMM data representation. Firstly, it is shown that the Expectation Maximization (EM) algorithm requires a scenario-specific initialization method to succeed. To solve this problem, a learning-based neural network is used to generate meaningful and discriminative HGMMs without the need for an initial condition. An adaptive modelling module utilizing the Non-maximum Suppression (NMS) algorithm is developed as a post-processing technique to detect and eliminate overlapping mixture components and hence reduce the number of required parameters for a more lightweight representation. For the second stage, a statistical primitive fitting module is applied to fit geometric primitives to the input point cloud based on the estimated HGMM. Then, a primitive alignment and merging algorithm is designed to locate and combine primitive segments that originally belong to a larger primitive model. This allows for a cleaner and more discriminative detection result. Finally, experiments are conducted to evaluate the performance of deep learning-based HGMM and the primitive detection results with the EM-based HGMMs as the baseline. The proposed system is able to provide a visual abstraction of the original 3D shapes with parameterized and simplified geometry format, which can later be applied in real-world applications such as 3D rendering, robot simulation, and game development.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".