TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2512.06269v1
• Date: Sat, 06 Dec 2025 03:45:39 GMT
• Title: TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting
• Authors: Quan Tran, Tuan Dang,
• Abstract summary: 3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render images.<n>This paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation.<n>We demonstrate the effectiveness of our method across multiple photorealistic datasets, achieving state-of-the-art results.
• Score: 2.441486089588484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render photorealistic images. However, building a 3D Gaussian is guided solely by photometric loss, which can result in inconsistencies in reconstruction. This under-constrained process often results in "floater" artifacts and unstructured geometry, preventing the extraction of high-fidelity surfaces. To address this issue, our paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation. Our approach aims to achieve a consensus on 3D representation in the physical world by utilizing various estimated views. We optimize this process by penalizing the deviation of a rendered 3D point from a robust consensus point, which is re-triangulated from a bundle of neighboring views in a self-supervised fashion. We demonstrate the effectiveness of our method across multiple datasets, achieving state-of-the-art results. On the DTU dataset, our method attains a mean Chamfer Distance of 0.50 mm, outperforming comparable explicit methods. We will make our code open-source to facilitate community validation and ensure reproducibility.

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