Uncertainty Feedback Loop for Camera Pose Precision
Figure 4 : Overview of the Uncertainty Feedback Loop. The framework establishes a dynamic link between view reliability and pose precision. (1) Initialization : Initial reliability is computed from feature correspondences ℳ i , j \mathcal{M}_{i,j} to provide a starting prior for camera uncertainty. (2) Dynamic Performance : A feedback buffer ℬ i \mathcal{B}_{i} tracks rolling PSNR scores to compute the dynamic confidence γ i ( t ) = ( 1 − α ) γ i ( 0 ) + α γ ^ i ( t ) \gamma_{i}^{(t)}=(1-\alpha)\gamma_{i}^{(0)}+\alpha\hat{\gamma}_{i}^{(t)} . (3) Probabilistic Optimization : The uncertainty
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Paper title: PCM-NeRF: Probabilistic Camera Modeling for Neural Radiance Fields under Pose Uncertainty Abstract: Neural surface reconstruction methods typically treat camera poses as fixed values, assuming perfect accuracy from Structure-from-Motion (SfM) systems. This assumption breaks down with imperfect pose estimates, leading to distorted or incomplete reconstructions. We present PCM-NeRF, a probabilistic framework that augments neural surface reconstruction with per-camera learnable uncertainty, built on top of SG-NeRF. Rather than treating all cameras equally throughout optimization, we represent each pose as a distribution with a learnable mean and variance, initialized from SfM correspondence quality. An uncertainty regularization loss couples the learned variance to view confidence, and the resulting uncertainty directly modulates the effective pose learning rate: uncertain cameras receive damped gradient updates, preventing poorly initialized views from corrupting the reconstruction. This lightweight mechanism requires no changes to the rendering pipeline and adds negligible overhead. Experiments on challenging scenes with severe pose outliers demonstrate that PCM-NeRF consistently outperforms state-of-the-art methods in both Chamfer Distance and F-Score, particularly for geometrically complex structures, without requiring foreground masks. Passages referencing this figure: is lightweight mechanism requires no changes to the rendering pipeline and adds negligible overhead. Experiments on challenging scenes with severe pose outliers demonstrate that PCM-NeRF consistently outperforms state-of-the-art methods in both Chamfer Distance and F-Score, particularly for geometrically complex structures, without requiring foreground masks. https://shravan-18.github.io/PCM-NeRF Figure 1 : Left: Qualitative reconstruction showing PCM-NeRF’s ability to capture fine surface detail. Right: PCM-NeRF outperforms all compared methods across both Chamfer Distance (lower is better) and F-Score (higher is better) * * footnotetext: Equal contribution. 1 Introduction Neural surface reconstruction (NSR) has become a dominant approach for recovering detailed 3D geometry from 2D images
