Adaptive Pyramid Context Fusion for Point Cloud Perception
Bibliographic record
Abstract
Deep learning for 3-D point cloud perception has been a very active research topic in recent years. A current trend is toward the combination of the semantically strong and the fine-grained information from different scales of intermediate representations to boost network generalization power and robustness against scale variation. One prominent challenge is how to effectively conduct the allocation of multiple scales of information. In this letter, we propose a module, named adaptive pyramid context fusion (APCF), to adaptively capture scales of contextual information from a multiscale feature pyramid for the point cloud. The APCF module reweights and aggregates the features from different levels in the feature pyramid via a softmax attention strategy. The allocation of information is adaptively conducted level by level from bottom to up first and then from top to bottom. To ensure both effectiveness and efficiency, we propose a multiscale context-aware network APCF-Net through applying our proposed APCF to the PointConv architecture. Experiments demonstrate that APCF-Net surpasses its vanilla counterpart by a large margin both in effectiveness and efficiency. Especially, APCF-Net outperforms state-of-the-art approaches on 3-D object classification and semantic segmentation task, with the overall accuracy of 93.3% on ModelNet40 and mIoU of 63.1% on ScanNet V2 online test.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".