Exploring State Space Models in LiDAR Point Cloud Segmentation
Bibliographic record
Abstract
Abstract. Mamba has achieved significant success in various fields due to its ability to efficiently model long-range dependencies with linear complexity. However, its application in LiDAR point cloud processing is still in its early stages, facing challenges such as unordered and irregular data structures. In this study, we investigated the performance of two existing Mamba-based algorithms, PointMamba and PointCloudMamba, on the aerial DALES LiDAR dataset for point cloud segmentation, and further explored the critical role of token serialization in influencing Mamba’s performance. To evaluate serialization quality, we proposed two novel indicators—Neighbor Preservation Ratio (NPR) and Sequence Jump Distance (SJD)—which quantify the ability of serialization methods to preserve spatial topology and geometric relationships. Our findings confirm the great potential of Mamba in LiDAR point cloud processing, and demonstrate that serialization significantly impacts Mamba’s performance, with better preservation of spatial and geometric relationships leading to higher segmentation accuracy. These results provide meaningful insights into improving Mamba’s performance in LiDAR point cloud processing and guiding the development of advanced serialization methods.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".