Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2506.03538v1
• Date: Wed, 04 Jun 2025 03:40:33 GMT
• Title: Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian Splatting
• Authors: Chengqi Li, Zhihao Shi, Yangdi Lu, Wenbo He, Xiangyu Xu,
• Abstract summary: 3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors.<n>Existing methods typically rely on strategies to handle the low-quality training data, which often struggle to produce stable and consistent reconstructions.<n>We propose Asymmetric Dual 3DGS, a novel framework that leverages the convergence nature of these artifacts.
• Score: 30.566082713819117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors. Existing methods typically rely on heuristic strategies to handle the low-quality training data, which often struggle to produce stable and consistent reconstructions, frequently resulting in visual artifacts. In this work, we propose Asymmetric Dual 3DGS, a novel framework that leverages the stochastic nature of these artifacts: they tend to vary across different training runs due to minor randomness. Specifically, our method trains two 3D Gaussian Splatting (3DGS) models in parallel, enforcing a consistency constraint that encourages convergence on reliable scene geometry while suppressing inconsistent artifacts. To prevent the two models from collapsing into similar failure modes due to confirmation bias, we introduce a divergent masking strategy that applies two complementary masks: a multi-cue adaptive mask and a self-supervised soft mask, which leads to an asymmetric training process of the two models, reducing shared error modes. In addition, to improve the efficiency of model training, we introduce a lightweight variant called Dynamic EMA Proxy, which replaces one of the two models with a dynamically updated Exponential Moving Average (EMA) proxy, and employs an alternating masking strategy to preserve divergence. Extensive experiments on challenging real-world datasets demonstrate that our method consistently outperforms existing approaches while achieving high efficiency. Codes and trained models will be released.

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