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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.023 |
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; both teacher heads agree on what is shown here.
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".