Super Generalized 4PCS for 3D Registration
Bibliographic record
Abstract
The 4-Points Congruent Sets (4PCS) Algorithm is an established approach to registering two overlapping 3D point sets with partial overlap and arbitrary initial poses. 4PCS performs the registration efficiently using a special set of 4 points, also known as a base, formed by two co-planar pairs of points within a RANSAC framework. The SUPER 4PCS algorithm uses intelligent indexing to reduce the complexity of the original 4PCS algorithm. Although SUPER 4PCS is efficient, we show in this work that one can gain significant practical improvements in runtime by reducing the number of congruent 4-point bases across the two 3D point sets. We accomplish this by using a generalized 4-point base which considers non-coplanar 4-point bases as well as planar ones. We show through experimentation that the number of 4-point bases decreases, sometimes exponentially, with a non-coplanar base. Using this property, we propose the Super Generalized 4PCS algorithm which can exhibit a significant speed-up of up to 6.5x over the Super 4PCS algorithm as demonstrated experimentally.
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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 | 0.003 |
| 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 | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".