Normal-NeRF: Ambiguity-Robust Normal Estimation for Highly Reflective Scenes

2025-01-16

AI Summary

To address surface normal ambiguity and geometric distortion in Neural Radiance Fields (NeRF) caused by specular reflections in highly reflective scenes, this paper proposes a transmission-gradient-driven ambiguity-robust normal estimation method—introducing, for the first time, gradient constraints along the transmission direction to regularize normal optimization. We design a dual-activation density module that decouples volumetric density from normal smoothness modeling, jointly preserving boundary sharpness and surface continuity. Furthermore, we integrate reflection-aware appearance modeling to achieve fine-grained geometric and photometric fidelity without strong geometric priors. Evaluated on multiple high-reflectivity datasets, our method significantly outperforms state-of-the-art approaches, improving both geometric reconstruction accuracy and rendering realism. It effectively suppresses over-smoothing while retaining subtle geometric structures and strong specular highlights.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Learning with Manifolds

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Neural Radiance Fields (NeRF) often struggle with reconstructing and rendering highly reflective scenes. Recent advancements have developed various reflection-aware appearance models to enhance NeRF's capability to render specular reflections. However, the robust reconstruction of highly reflective scenes is still hindered by the inherent shape ambiguity on specular surfaces. Existing methods typically rely on additional geometry priors to regularize the shape prediction, but this can lead to oversmoothed geometry in complex scenes. Observing the critical role of surface normals in parameterizing reflections, we introduce a transmittance-gradient-based normal estimation technique that remains robust even under ambiguous shape conditions. Furthermore, we propose a dual activated densities module that effectively bridges the gap between smooth surface normals and sharp object boundaries. Combined with a reflection-aware appearance model, our proposed method achieves robust reconstruction and high-fidelity rendering of scenes featuring both highly specular reflections and intricate geometric structures. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on various datasets.

Problem

• NeRF
• Shiny and Reflective Scenes
• Surface Normal and Detail Reconstruction

Innovation

• Normal-NeRF
• Enhanced Boundary Clarity
• Super Shiny Scene Rendering