[Submitted on 4 Nov 2026]
Title:T$^2$-Splat: Adaptive Topology Mesh Splatting with Texture Residuals
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Abstract:Triangle-based splatting connects novel view synthesis with conventional graphics pipelines, but often requires excessive tessellation to preserve appearance details. We present T$^2$-Splat, which decouples topology adaptation from appearance refinement. Safe Restricted Delaunay Triangulation (Safe RDT) initializes mesh connectivity. Dynamic Topology Evolution (DTE) then adapts triangle allocation through edge collapse and vertex split operations. Residual per-face Mip-textures then refine appearance on the fixed mesh using scale-adaptive filtering. Experiments on Mip-NeRF 360, Tanks & Temples, and DTU demonstrate competitive results with compact meshes. T$^2$-Splat improves PSNR by 0.71dB over MeshSplatting on Mip-NeRF 360. Additional compression experiments demonstrate flexible control of mesh complexity, supporting face budgets as low as 10% of the original face count. Project page: this https URL.
Comments: Accepted at NeurIPS 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
Cite as: arXiv:2611.06974 [cs.CV]
(or arXiv:2611.06974v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.06974
arXiv-issued DOI via DataCite
Submission history
From: Zhihao Tang [view email] [v1] Wed, 4 Nov 2026 13:09:22 UTC (15,174 KB)
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