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Graph-Guided 3D Gaussian Splatting —Deep Dive (Part 2)
- COLMAP's major drawback of exhaustive pairwise feature matching is addressed by Graph GS, which uses smarter matching for efficiency as datasets grow larger.
- Graph GS replaces COLMAP with DUSt3r to initialize the system without expensive feature-based matching.
- CNNP in Graph GS balances matching by building a graph using constrained nearest neighbors, ensuring stable local bundle adjustments.
- Graph GS introduces concentric matching by sampling image pairs from concentric rings around each camera for structural consistency.
- Sequential matching in Graph GS connects cameras captured in a sequence, ensuring stability and consistent motion.
- Graph GS utilizes a filtering process to remove noisy or misleading matches, enhancing the accuracy of 3D reconstruction.
- To ensure accurate matching, Graph GS uses a state table with strict and loose filtering modes based on probability calculations.
- Graph GS employs an octree-based pruning method to optimize processing efficiency by focusing on meaningful spatial regions.
- In camera graph construction, Graph GS connects image pairs as edges, assigning weights based on pose estimates for coordinated optimization across images.
- MVCC in Graph GS enforces consistency among multiple camera views based on edge weights, preventing overfitting and enhancing data augmentation.
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