在野外环境中实现鲁棒神经渲染：基于非对称双3D高斯溅射的方法

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

June 4, 2025

作者: Chengqi Li, Zhihao Shi, Yangdi Lu, Wenbo He, Xiangyu Xu

cs.AI

摘要

由于光照条件的不一致性和瞬态干扰物的存在，从野外图像进行三维重建仍是一项具有挑战性的任务。现有方法通常依赖启发式策略来处理低质量的训练数据，这些策略往往难以生成稳定且一致的重建结果，常导致视觉伪影的出现。在本研究中，我们提出了非对称双3DGS框架，该框架巧妙地利用了这些伪影的随机性特征：由于微小的随机性，它们在不同训练运行中会有所变化。具体而言，我们的方法并行训练两个3D高斯溅射（3DGS）模型，通过施加一致性约束，促使模型在可靠的场景几何上收敛，同时抑制不一致的伪影。为了防止两个模型因确认偏误而陷入相似的失败模式，我们引入了一种分叉掩码策略，应用两种互补的掩码：多线索自适应掩码和自监督软掩码，从而引导两个模型进行非对称训练，减少共享的错误模式。此外，为了提高模型训练的效率，我们提出了一种轻量级变体——动态EMA代理，该变体将其中一个模型替换为动态更新的指数移动平均（EMA）代理，并采用交替掩码策略以保持分叉性。在具有挑战性的真实世界数据集上进行的大量实验表明，我们的方法在保持高效率的同时，始终优于现有方法。代码及训练模型将予以公开。

English

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.