Bibliographic record
Abstract
The dataset introduced in "PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting" DesktopObjects-360, a benchmark specifically designed for 3D segmentation for radiance field (3DGS and NeRF). The key features include: 1. The dataset provides COLMAP format data, making it directly applicable to 3DGS and NeRF modeling. 2. 3D Instance annotations are available for Gaussian models. 3. Pixel-level instance segmentation annotations are provided for 2D images. 4. The instance IDs for 2D and 3D objects remain consistent across different viewpoints. The dataset contains a total of 3,364 multi-view images with fine-grained 2D and 3D instance annotations. Each scene includes 7--10 object instances (56 annotated 3D instances in total), covering common desktop items under varying layouts and occlusions. To support instance segmentation tasks, we provide 26,042 pixel-accurate 2D instance masks across all scenes, with an average of 465 masks per 3D instance, ensuring dense and consistent multi-view correspondence. The DesktopObjects-360 dataset is organized in a hierarchical directory structure, with a root folder containing six subdirectories (Desk1 through Desk6) and one PointCloud.7z. Each Desk folder follows a consistent organization scheme, as exemplified by Desk1 which contains subfolders for 1. test data (Desk1_test), 2. original images (images), 3. segmentation masks (mask), 4. visualized masks (mask_visualize), 5. pretrained model annotations (annotated\_pretrained_model(2dgs)), 6. and a class label file (class.txt). In addition, we also provide pre-trained 2D Gaussian Splatting (2DGS) models with annotations for 10 scenes from the Nerds360 dataset.
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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) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.049 |
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".