A Comparative Study of Semantic Segmentation using Deep Neural Networks in a GNSS-denied Underground Parking Lot
Bibliographic record
Abstract
Abstract. Deep neural networks (DNNs) in intelligent point cloud processing have achieved remarkable progress in recent years. Most existing methods and models were adopted on either outdoor or indoor scenes while very few previous studies were conducted in GNSS-denied environments. In this paper, we carried out a comparative study in semantic segmentation outputs using different DNNs in an underground parking lot dataset. Manually labeled indoor point cloud data were trained and tested using 7 different DNNs (e.g. PointNet, KPConv, FPConv, BAAF-Net, etc.). Our experiments demonstrated how well different DNNs perform in GNSS-denied environments with performance assessments in mIoU, Mean Accuracy (mAcc), Overall Accuracy (OA), as well as visualization outputs. The main contribution of this comparative study is to compare state-of-the-art DNN algorithms’ performance in semantic segmentation directly on the raw indoor mobile laser scanning (iMLS) data from a GNSS-denied underground parking lot and evaluate the effectiveness and potentials of different DNNs in underground 3D taskings. Draw upon that, which current algorithms are optimal and how future work in GNSS-denied environments can be inspired and implemented would be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".