Dense Reconstruction from Visual SLAM with Probabilistic Multi-Sequence Merging
Bibliographic record
Abstract
This paper presents a comprehensive visual SLAM system that extends the application of ORB-SLAM3. Using it as a template, a supplementary and optional function of 3D dense reconstruction is implemented for both RGB-D and stereo cameras. With conventional datasets, TUM, EuRoC, and KITTI as benchmarks, we confirm the validity of proposed system in both indoor and outdoor scenarios. Besides, the concept of Octree is integrated into our system to generate Octomap. A compact mapping can be achieved as such, verified by the fact that the size of each dense point cloud map is reduced to approximately one-fifth after the conversion. Furthermore, a multi-sequence merging method is included in our proposed system, formulating with a probabilistic-based optimizing algorithm and map accessing functions from the original system. Multi-sequence experiments evince that the tracking accuracy profits from the exploitation of a priori knowledge gathered through the preceding sequences.
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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 | 0.000 |
| 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".