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Published as a conference paper at NeurIPS 2026

T-Splat: Adaptive Topology Mesh Splatting with Texture Residuals

Zhihao Tang, Youjia Zhang, Mingbo Zhao, Wei Yang

Abstract

Triangle-based splatting has emerged as a promising bridge between high-quality novel view synthesis and traditional graphics pipelines. However, naively applying unstructured splatting mechanisms to polygonal primitives fails to exploit their inherent topological advantages, leading to over-tessellation and degraded high-frequency appearances. To address, we present T-Splat, a mesh splatting framework with adaptive topology and texture optimization. We introduce progressive edge-collapse and vertex-split operations to adaptively allocate triangle primitives, reducing face count while preserving well-conditioned connectivity. We further propose a scale-aware residual Mip-texture model to decouple appearance from geometry, for providing the topology with flexibility needed to safely collapse redundant triangles without introducing blurring or aliasing. Experiments on the Mip-NeRF 360, Tanks & Temples, and DTU benchmarks demonstrate that T-Splat improves PSNR by +0.87 dB and maintains robust visual fidelity even under aggressive mesh compression to 10% of the original face count.

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