Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Neural Radiance Fields (NeRFs) offer versatility and robustness in map representations for Simultaneous Localization and Mapping (SLAM) tasks. This paper extends NICE-SLAM, a recent state-of-the-art NeRF-based SLAM algorithm capable of producing high quality NeRF maps. However, depending on the hardware used, the required number of iterations to produce these maps often makes NICE-SLAM run at less than real time. Additionally, the estimated trajectories fail to be competitive with classical SLAM approaches. Finally, NICE-SLAM requires a grid covering the considered environment to be defined prior to runtime, making it difficult to extend into previously unseen scenes. This paper seeks to make NICE-SLAM more open-world-capable by improving the robustness and tracking accuracy, and generalizing the map representation to handle unconstrained environments. This is done by improving measurement uncertainty handling, incorporating motion information, and modelling the map as having an explicit foreground and background. It is shown that these changes are able to improve tracking accuracy by 85% to 97% depending on the available resources, while also improving mapping in environments with visual information extending outside of the predefined grid.
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.
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 | 0.001 |
| 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 it