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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".