Bibliographic record
Abstract
Point cloud shape completion aims to reconstruct complete point clouds from partial point clouds. The denser point clouds are, the richer information they contain. Different from existing methods that pay more attention to sparse completion of point clouds and global feature information of partial point clouds, this paper proposes a novel dense point cloud completion network called N-DPC, which combines self attention unit with the fusion of local feature and global feature information. First, we apply self attention unit to point clouds' global feature extraction to make it lay emphasis on the dependency between different points. Second, we adopt the method of multi-stage completion where we obtain coarse point clouds at first stage and combine local and global information to achieve the completion of dense point clouds. Quantitative and qualitative evaluations of experiments demonstrate that the proposed method has achieved better performance on ShapeNet dataset compared with existing state-of-the-art point cloud completion works and shows a good robustness for different missing ratios of point clouds. Additionally, the proposed N-DPC is valid for real point clouds on KITTI 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 | 0.000 | 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".