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

TL;DR

This paper introduces Asymmetric Dual 3D Gaussian Splatting, a novel neural rendering framework that enhances 3D reconstruction stability and quality in challenging real-world conditions by leveraging model divergence and consistency constraints.

Contribution

The work proposes a dual-model training strategy with asymmetric masking and a lightweight EMA proxy to improve robustness and efficiency in neural rendering from in-the-wild images.

Findings

Outperforms existing methods on real-world datasets

Achieves higher reconstruction stability and visual quality

Reduces training time with lightweight model variants

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 \modelname{}, 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. See the project website at https://steveli88.github.io/AsymGS.