Gated multi-source fusion with geometric sequence modeling for novel urban structure discovery
Bibliographic record
Abstract
Novel Class Discovery (NCD) in 3D point cloud semantic segmentation presents critical challenges for urban management systems, where models must segment previously unseen object classes in rapidly evolving urban environments. Traditional 3D semantic segmentation models struggle to adapt to heterogeneous spatial characteristics and complex geometric structures of urban point clouds, limiting their ability to handle novel objects without extensive retraining. This paper introduces Adaptive Geometric Discovery Network (AGDNet), a comprehensive framework enhancing NCD through three key innovations: Adaptive Geometric Sequence Modeling module (AGSM), Dynamic Gaussian Embedding module (DGE), and Gated Multi-Source Feature Fusion module (GMSFF). AGSM addresses heterogeneous spatial characteristics through density-aware adaptive sampling, dynamic grouping, and multi-aspect geometric feature encoding. DGE represents point clouds as learnable 3D Gaussians parameterized by position, scale, orientation, and features, providing continuous probabilistic representations capturing both local geometric details and global spatial contexts. GMSFF integrates features from AGSM, DGE, and MinkowskiNet through context-aware gating mechanisms. The framework introduces three specialized knowledge transfer objectives for NCD: Prototype Relation Loss establishes semantic connections between known and novel class prototypes; Contrastive Alignment Loss creates instance-level semantic bridges; and Semantic Transfer Loss enables distribution-based knowledge propagation. These objectives bridge the semantic gap between known and novel categories while mitigating class imbalance challenges. Comprehensive evaluation on Toronto-3D, SemanticSTF, and SemanticPOSS datasets demonstrates significant improvements over state-of-the-art methods NOPS and CHNCD. For novel class discovery, the framework achieves average improvements of 6.47%/3.48%, 4.61%/3.12%, and 6.64%/4.24% in novel class mean Intersection over Union (mIoU) over NOPS/CHNCD respectively. For overall performance, improvements reach 6.59%/3.88%, 7.32%/4.80%, and 7.27%/4.62% in overall mIoU. These results validate the framework’s effectiveness for urban management, environmental monitoring, and infrastructure planning applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".