A Feature-Driven Approach to Semantic Segmentation in Large-Scale 3D Urban Dataset
Bibliographic record
Abstract
Abstract. Urban environments are continually evolving, which presents significant challenges for 3D semantic segmentation systems that must adapt to emerging object categories. In this paper, we address the problem of Novel Class Discovery (NCD) in 3D semantic segmentation for urban scenes. We introduce a feature-driven framework that leverages the Dynamic Multi-level Feature Synthesis Module (D-MFSM) to extract and integrate multi-scale, cross-view structural information from raw urban point clouds. D-MFSM dynamically partitions point clouds via an adaptive grouping mechanism that utilizes a learnable spatial weight vector, and subsequently constructs local neighborhoods by means of an improved farthest point sampling strategy. The extracted local features are then processed by a dual-path adaptive synthesis mechanism and further refined through a novel cross-axis reordering strategy, which together yield comprehensive aggregated feature representations. These representations facilitate robust novel class discovery while maintaining high segmentation accuracy on known classes. Comprehensive evaluations on the DALES dataset demonstrate that the proposed approach yields substantial improvements in segmentation performance across diverse urban scenarios. The proposed framework, therefore, offers a complementary solution to existing methods and contributes to the development of more adaptive and accurate 3D semantic segmentation systems in complex urban settings.
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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) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".