Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian
  Splatting
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.
6841585dd777f13c59460b47
Chengqi Li
admin_assigned
2025-06-05T12:59:09.705Z
66390dcf3ec6a9595ab36b73
/avatars/a93e2471082b52abcb018db5ff1e8032.svg
Chengqi Li
user
lichengqi617
6841585dd777f13c59460b48
Zhihao Shi
6841585dd777f13c59460b49
Yangdi Lu
admin_assigned
2025-06-05T12:58:57.071Z
6444ca1a1cfc9ae6bb3a0bd0
/avatars/599a40c5bd1a93d87740c594f5069fe7.svg
yang
user
yangdilu
6841585dd777f13c59460b4a
Wenbo He
admin_assigned
2025-06-05T12:58:50.568Z
668a09f43e1066772fdb5364
/avatars/c23accd322ab276f750dd53117fee352.svg
Wenbo He
user
hewb14
6841585dd777f13c59460b4b
Xiangyu Xu
extracted_pending
2025-06-05T08:42:10.367Z
634e60454677a5891c0902f4
/avatars/4dc143719afe7686e05b7f2c2c5c1871.svg
Xiangyu Xu
user
xjcvcvxj
Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian
  Splatting
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.
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.
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