BERT-Enhanced with Context-Aware Embedding for Instance Segmentation in 3D Point Clouds
Bibliographic record
Abstract
Inspired by the successful implementation of transformer network in the Natural Language Processing (NLP), we propose a novel Bidirectional Encoder Representations from Transformers (BERT)-based point cloud segmentation method. Specifically, the whole point cloud is scanned by multiple overlapping windows. We made the first attempt ever to input each window-point-cloud into the BERT model which outputs points' semantic labels and high-dimensional context-aware point embeddings. In the process of training, the Kullback-Leibler (KL)-Divergence-based clustering loss is utilized to optimize the network's parameters by calculating similarity matrices between the point embeddings and the predicted semantic labels. The final instance labels can be obtained by softmax function on these optimized point embeddings. By evaluating on the Stanford 3D Indoor Scene (S3DIS) dataset, our proposed method has reached a micro-mean accuracy (mAcc) of 87.3% on the semantic segmentation task and an Average Precision (mAP) on the instance segmentation task. The results on both tasks have surpassed the traditional point cloud segmentation models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".