Visual SLAM Fusing Robust Segmentation of Moving Targets and NeRF
Bibliographic record
Abstract
While existing systems combining neural implicit representation and visual SLAM perform well in static environments, they usually face challenges in real-world scenes, where localization accuracy and robustness are affected by moving targets. To address the problem, we propose a dense neural implicit SLAM system to accommodate dynamic scenes. We utilize an instance segmentation network to split the scene, perform multi-object tracking on potential moving objects, and discriminate dynamic targets based on motion consistency between static background and tracked objects. After that, we exclude dynamic object area to improve accuracy of pose estimation, thereby enhancing robustness of system. Then, we use multi-resolution hash coding for 3D rendering to achieve dense reconstruction of static scenes in dynamic environments. Experimental evaluations conducted on TUM and Bonn datasets demonstrate better localization accuracy and 3D reconstruction of static backgrounds compared to existing dynamic visual SLAM systems.
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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".