用特征与深度一致性提升稀疏视角下的3D表面重建质量
SparseRecon: Neural Implicit Surface Reconstruction from Sparse Views with Feature and Depth Consistencies
- 引入跨视角特征一致性损失，增强隐式场约束
- 不确定性引导的深度约束提升遮挡区几何细节
- 适合低重叠视角场景，尤其适用于数据稀缺任务
从稀疏视角进行表面重建旨在仅凭少量RGB图像重构3D形状或场景。现有方法分为泛化型和过拟合型：前者在训练中未见视角上泛化能力差，后者受限于几何线索不足，重建质量有限。为此，我们提出SparseRecon，一种基于体渲染的特征一致性与不确定性引导深度约束的神经隐式重建方法。首先，设计跨视角特征一致性损失，缓解视图间一致性信息不足带来的模糊性，确保重建结果完整且平滑。其次，在遮挡区与特征不显著区域引入不确定性引导的深度约束，补充几何细节，提升重建质量。实验表明，该方法优于当前最先进方法，在小重叠视角场景下仍能生成高质量几何结构。项目主页：https://hanl2010.github.io/SparseRecon/
原文摘要 · Abstract (English)
Surface reconstruction from sparse views aims to reconstruct a 3D shape or scene from few RGB images. The latest methods are either generalization-based or overfitting-based. However, the generalization-based methods do not generalize well on views that were unseen during training, while the reconstruction quality of overfitting-based methods is still limited by the limited geometry clues. To address this issue, we propose SparseRecon, a novel neural implicit reconstruction method for sparse views with volume rendering-based feature consistency and uncertainty-guided depth constraint. Firstly, we introduce a feature consistency loss across views to constrain the neural implicit field. This design alleviates the ambiguity caused by insufficient consistency information of views and ensures completeness and smoothness in the reconstruction results. Secondly, we employ an uncertainty-guided depth constraint to back up the feature consistency loss in areas with occlusion and insignificant features, which recovers geometry details for better reconstruction quality. Experimental results demonstrate that our method outperforms the state-of-the-art methods, which can produce high-quality geometry with sparse-view input, especially in the scenarios with small overlapping views. Project page: https://hanl2010.github.io/SparseRecon/.
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