DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds
Bibliographic record
Abstract
Semantic segmentation of large-scale point clouds is essential for applications such as autonomous driving and high-definition mapping. However, this task remains challenging due to the imbalanced distribution of categories in large-scale point cloud data and the similarity in local geometric structures. Most current deep learning–based methods concentrate on designing local feature extraction modules while neglecting the significance of long-distance contextual information. Nevertheless, this contextual information is crucial for accurate object segmentation in large-scale scenes. To address this limitation, we propose a dual-encoder segmentation network called DE-Net. DE-Net effectively learns both the local and long-distance contextual information for each point to achieve accurate point segmentation. DE-Net consists of two main components: dual-encoder modules (DEMs) and gradient-aware pooling modules (GAPM). DEMs extract local geometry and long-distance contextual information for each point using positional and trigonometric encoding to distinguish complex geometric features. GAPMs aggregate global information effectively using dual-distance andxygradient information. In addition, a prediction jitter module was introduced during training to address the issue of class imbalance and improve the network's prediction results. The experimental results on three public benchmarks demonstrate that DE-Net outperforms existing state-of-the-art methods, achieving mean intersection over union scores of 83.5%, 61.8%, and 63.9% on Toronto-3D, WHU-MLS, and S3DIS datasets, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".