In recent years, the neural implicit surface has emerged as a powerful
representation for multi-view surface reconstruction due to its simplicity and
state-of-the-art performance. However, reconstructing smooth and detailed
surfaces in indoor scenes from multi-view images presents unique challenges.
Indoor scenes typically contain large texture-less regions, making the
photometric loss unreliable for optimizing the implicit surface. Previous work
utilizes monocular geometry priors to improve the reconstruction in indoor
scenes. However, monocular priors often contain substantial errors in thin
structure regions due to domain gaps and the inherent inconsistencies when
derived independently from different views. This paper presents \textbf{DebSDF}
to address these challenges, focusing on the utilization of uncertainty in
monocular priors and the bias in SDF-based volume rendering. We propose an
uncertainty modeling technique that associates larger uncertainties with larger
errors in the monocular priors. High-uncertainty priors are then excluded from
optimization to prevent bias. This uncertainty measure also informs an
importance-guided ray sampling and adaptive smoothness regularization,
enhancing the learning of fine structures. We further introduce a bias-aware
signed distance function to density transformation that takes into account the
curvature and the angle between the view direction and the SDF normals to
reconstruct fine details better. Our approach has been validated through
extensive experiments on several challenging datasets, demonstrating improved
qualitative and quantitative results in reconstructing thin structures in
indoor scenes, thereby outperforming previous work.

本文提出了 DebSDF，旨在解决多视角图像室内场景中由于纹理缺乏和领域差异导致的误差问题，并通过不确定性建模、重要性引导射线采样以及有偏的密度变换等方法改进了细节重建，实验证明了其在重建室内场景中细结构方面的优越性。