INF-PCA: Implicit Neural Field-Based Interactive Point Cloud Semantic Annotation
Bibliographic record
Abstract
Point cloud semantic segmentation helps Intelligent Transportation Systems understand traffic scenes by assigning semantic label to each point in the point cloud, and it relies on large amounts of annotated training data. Nevertheless, manually annotating large-scale datasets of complex traffic scenes is quite time-consuming and tedious. This paper proposes INF-PCA, an interactive point cloud semantic annotation method based on implicit neural field, which allows users to achieve high-quality, large-scene and fast-response semantic annotation with only a few dozen mouse clicks. Firstly, the appearance, geometry and semantics of the point clouds are jointly represented by an implicit neural field, which maps a 3D spatial coordinate to its corresponding attributes. Secondly, an uncertainty-based semantic entropy loss and a supervoxel-based local consistency loss are designed to force the network to produce deterministic predictions with local consistency, thus generating smoother and more accurate boundaries. Furthermore, an active learning-based strategy for click-free annotation is proposed and analyzed to further reduce annotation pressure. Comprehensive experiments on multiple datasets including the road scene dataset Toronto3D revealed that INF-PCA can achieve more accurate annotations with faster response speed and only half of the clicks employed by the state-of-the-art methods, and that INF-PCA can be directly applied to intelligent transportation applications such as interactive segmentation of road scenes, inventory of transportation infrastructure assets, and production of high-definition map.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".