title: Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian
    Splatting
summary: 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.
paper_authors._id: 6841585dd777f13c59460b47
paper_authors.name: Chengqi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:59:09.705Z
paper_authors._id: 66390dcf3ec6a9595ab36b73
paper_authors.avatarUrl: /avatars/a93e2471082b52abcb018db5ff1e8032.svg
paper_authors.fullname: Chengqi Li
paper_authors.type: user
paper_authors.user: lichengqi617
paper_authors._id: 6841585dd777f13c59460b48
paper_authors.name: Zhihao Shi
paper_authors._id: 6841585dd777f13c59460b49
paper_authors.name: Yangdi Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:58:57.071Z
paper_authors._id: 6444ca1a1cfc9ae6bb3a0bd0
paper_authors.avatarUrl: /avatars/599a40c5bd1a93d87740c594f5069fe7.svg
paper_authors.fullname: yang
paper_authors.type: user
paper_authors.user: yangdilu
paper_authors._id: 6841585dd777f13c59460b4a
paper_authors.name: Wenbo He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:58:50.568Z
paper_authors._id: 668a09f43e1066772fdb5364
paper_authors.avatarUrl: /avatars/c23accd322ab276f750dd53117fee352.svg
paper_authors.fullname: Wenbo He
paper_authors.type: user
paper_authors.user: hewb14
paper_authors._id: 6841585dd777f13c59460b4b
paper_authors.name: Xiangyu Xu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-05T08:42:10.367Z
paper_authors._id: 634e60454677a5891c0902f4
paper_authors.avatarUrl: /avatars/4dc143719afe7686e05b7f2c2c5c1871.svg
paper_authors.fullname: Xiangyu Xu
paper_authors.type: user
paper_authors.user: xjcvcvxj
paper_title: Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian
    Splatting
paper_summary: 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.
paper_ai_summary: A novel Asymmetric Dual 3DGS framework improves 3D reconstruction by training dual models with consistency constraints and divergent masking, outperforming existing methods with high efficiency.
paper_ai_keywords: 3D reconstruction 3D Gaussian Splatting (3DGS) stochastic artifacts consistency constraint confirmation bias divergent masking multi-cue adaptive mask self-supervised soft mask Dynamic EMA Proxy lightweight variant Exponential Moving Average (EMA) alternating masking strategy