A Probability-Guided Sampler for Neural Implicit Surface Rendering
To address inefficient ray sampling and insufficient reconstruction fidelity for foreground implicit surfaces in Neural Radiance Fields (NeRF), this paper proposes an adaptive sampling framework explicitly targeting foreground implicit surfaces. The method models a differentiable probability density function (PDF) directly in the image projection space to guide dense ray sampling within regions of interest. Furthermore, it introduces a novel surface reconstruction loss that jointly optimizes the implicit surface and radiance field by integrating near-surface geometric priors with free-space constraints. Crucially, the approach requires no additional supervision or pretraining and consistently improves mainstream NeRF variants. It significantly enhances geometric accuracy and detail fidelity in target regions while reducing redundant sampling overhead. Experiments demonstrate substantial gains across standard metrics—PSNR, SSIM, and LPIPS—particularly in complex scenes.