Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a method for multi-view 3D robotic object recognition targeted for cluttered indoor scenes. We explicitly model occlusions that cause failures in visual detectors by learning a generative appearance-occlusion model from a training set containing annotated 3D objects, images and point clouds. A Bayesian 3D object likelihood incorporates visual information from many views as well as geometric priors for object size and position. An iterative, sampling-based inference technique determines object locations based on the model. We also contribute a novel robot-collected data set with images and point clouds from multiple views of 60 scenes, with over 600 manually annotated 3D objects accounting for over ten thousand bounding boxes. This data has been released to the community. Our results show that our system is able to robustly recognize objects in realistic scenes, significantly improving recognition performance in clutter.
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 | 0.000 |
| 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 it