PiPCS: Perspective Independent Point Cloud Simplifier for Complex 3D Indoor Scenes
Bibliographic record
Abstract
The emergence of commercially accessible depth sensors has driven the widespread adoption of 3D data, offering substantial benefits across diverse applications, ranging from human activity recognition to augmented reality. However, indoor environments present significant challenges for 3D computer vision applications, particularly in cluttered and dynamic scenes where background bounding surfaces hinder the detection and analysis of foreground objects. We introduce a novel perspective-independent point cloud simplifier (PiPCS) for complex 3D indoor scenes. PiPCS streamlines 3D computer vision workflows by contextually segmenting and subtracting background bounding surfaces and preserving segmented foreground objects within indoor scenes, effectively reducing the size of indoor point clouds and enhancing 3D indoor scene perception. Methodologically, we use estimated surface normals to intelligently divide an input point cloud into distinct zones, which are then split into multiple distinct parallel clusters. Next, we find the largest plane in each cluster and sort the fitted planes within each zone based on their distance along the zone’s normal vector to identify the bounding surfaces. Finally, we segment the 3D background, simplify the point cloud by employing a voxel-based background subtraction technique, and segment 3D foreground objects via a cluster-based segmentation approach. We evaluated PiPCS on the Stanford S3DIS dataset and our own challenging dataset, achieving average values of 97.08% for specificity and 91.27% for F1 score on the S3DIS dataset and size reductions averaging 74.11% overall. Our experimental and evaluation results demonstrate that PiPCS robustly simplifies and reduces the size of unorganized indoor point clouds.
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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) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".