CSG-Fusion: Consistent Sparse-View Gaussian Splatting via Matching-based Fusion
Bibliographic record
Abstract
Recent developments in Gaussian splatting have enabled high-fidelity 3D reconstruction from multi-view images, but pixel-aligned methods such as MASt3R often produce redundant primitives and inconsistent geometry under few-view settings. We propose CSG-Fusion, a feed-forward framework that mindfully integrates pixel-aligned pointmap to reduce redundant primitives and produce compact and consistent 3D structures. Our approach leverages a matching prior with spatial thresholds to prune overlapping Gaussians, forming a coherent base 3D model, and then applies a mask-based feature aggregation module to merge local features and improve photometric consistency with fewer primitives. To enforce cross-view agreement after fusion, we further incorporate context-view supervision to align appearance and geometry across perspectives. Experiments on the large-scale ScanNet++ and object-level DTU benchmarks demonstrate both the efficiency and generalization of our method. Compared to the leading pose-known and pose-free approaches, our method achieves higher rendering quality with substantially fewer Gaussians.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".