Bibliographic record
Abstract
3D scanners have become widely used in many industrial applications in reverse engineering, quality inspection, entertainment industry, etc. Despite the popularity of 3D scanners, the raw scanned data, referred to as point cloud, is often contaminated by outliers not belonging to the scanned surface. Moreover, when the scanned surface is highly reflective, outliers become much more extensive due to specular reflections. Such outliers cause considerable issues to point cloud applications and thus need to be removed through an outlier detection process. Considering the commonness of reflective surfaces in mechanical parts, it is critical to investigate the outlier formation mechanism and develop methods to effectively remove outliers. However, research on how outliers are formed in scanning reflective surfaces is very limited. Meanwhile, existing outlier removal methods show limited effectiveness in detecting extensive outliers. This thesis investigates the outlier formation mechanism in scanning reflective surfaces using laser scanners, and develops outlier removal algorithms to effectively and efficiently detect outliers in the scanned point clouds. The overall objective is to remove outliers in a raw data to obtain a clean point cloud in order to ensure the performance of point cloud applications. In particular, two outlier formation models, mixed reflections and multi-path reflections, are proposed and verified through experiments. The effects of scanning orientation on outlier formation are also experimentally investigated. A guidance of proper scan path planning is provided in order to reduce the occurrence of outliers. Regarding outlier removal, a rotating scan approach is proposed to efficiently remove view-dependent outliers. A flexible and effective algorithm is also presented to detect the challenging non-isolated outliers as well as other outliers.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".