The parallel iterative closest point algorithm
Bibliographic record
Abstract
This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspondence calculations are distributed among the processor resources. At the end of each iteration, the results of the correspondence determination are communicated back to a central processor and the current transformation is calculated A number of additional techniques were developed that served to improve upon this basic scheme. Calculating the partial sums within each distributed resource made it unnecessary to transmit the correspondence values back to the central processor, which reduced the communication overhead, and improved time performance. Randomly distributing the points among the processor resources resulted in a better load balancing, which further improved time performance. We also found that thinning the image by randomly removing a certain percentage of the points did not improve the performance, when viewed as the progression of mse with time. The method was implemented and tested on a 22 node Beowulf class cluster. For a large image, linear performance improvements were obtained for up to 16 processors, while they held for up to 8 processors with a smaller image.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".