TopoGS: Topology-Aware Anchor Feature Aggregation for Large-Scale 3D Gaussian Splatting

arXiv:2609.27868v1 Announce Type: cross Abstract: Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are typically optimized independently, leaving the octree topology underused during feature learning. We observe that uniform cross-level aggregation produces asymmetric effects: fine-level anchors benefit from coarse context, whereas coarse-level anchors require selective information from their descendants. We therefore propose TopoGS, a topology-aware anchor feature aggregation framework with two lightweight components. Hierarchical Anchor Coupling establishes bidirectional cross-level gradient pathways by fusing per-level context triplets with a residual MLP. Structure-Aware Containment Aggregation uses octree containment and hash-based matching to distinguish anchors with valid parent-child relations from isolated anchors, then applies soft weighting to accommodate varying topological sparsity. Experiments on ten scenes from Mill19, UrbanScene3D, Tanks & Temples, MatrixCity, and WHU show consistent improvements over state-of-the-art methods. TopoGS achieves average PSNR gains of 2.13, 1.78, and 0.29 dB over the strongest reported baseline on aerial, ground-level, and synthetic-cartographic scenes, respectively, while rendering faster and using less memory. Code is available at https://github.com/WZ-CS/TopoGS.

Abstract:Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are typically optimized independently, leaving the octree topology underused during feature learning. We observe that uniform cross-level aggregation produces asymmetric effects: fine-level anchors benefit from coarse context, whereas coarse-level anchors require selective information from their descendants. We therefore propose TopoGS, a topology-aware anchor feature aggregation framework with two lightweight components. Hierarchical Anchor Coupling establishes bidirectional cross-level gradient pathways by fusing per-level context triplets with a residual MLP. Structure-Aware Containment Aggregation uses octree containment and hash-based matching to distinguish anchors with valid parent-child relations from isolated anchors, then applies soft weighting to accommodate varying topological sparsity. Experiments on ten scenes from Mill19, UrbanScene3D, Tanks & Temples, MatrixCity, and WHU show consistent improvements over state-of-the-art methods. TopoGS achieves average PSNR gains of 2.13, 1.78, and 0.29 dB over the strongest reported baseline on aerial, ground-level, and synthetic-cartographic scenes, respectively, while rendering faster and using less memory. Code is available at this https URL.

9 figures, 7 tables; supplementary material included. Code is available at this https URL

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2609.27868 [cs.CV]

(or arXiv:2609.27868v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.27868 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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TopoGS: Topology-Aware Anchor Feature Aggregation for Large-Scale 3D Gaussian Splatting

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