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arXiv · 2503.11601

Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information

Xuanqi Zhang·Jieun Lee·Chris Joslin·Wonsook Lee
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Abstract

We present a novel framework for enhancing the visual fidelity and consistency of text-guided 3D Gaussian Splatting (3DGS) editing. Existing editing approaches face two critical challenges: inconsistent geometric reconstructions across multiple viewpoints, particularly in challenging camera positions, and ineffective utilization of depth information during image manipulation, resulting in over-texture artifacts and degraded object boundaries. To address these limitations, we introduce: 1) A complementary information mutual learning network that enhances depth map estimation from 3DGS, enabling precise depth-conditioned 3D editing while preserving geometric structures. 2) A wavelet consensus attention mechanism that effectively aligns latent codes during the diffusion denoising process, ensuring multi-view consistency in the edited results. Through extensive experimentation, our method demonstrates superior performance in rendering quality and view consistency compared to state-of-the-art approaches. The results validate our framework as an effective solution for text-guided editing of 3D scenes.

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Xuanqi Zhang, Jieun Lee, Chris Joslin, Wonsook Lee. 2025-03-14. Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information. https://arxiv.org/abs/2503.11601

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