提升无限场景3D重建精度，兼顾光照变化与度量准确性
CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenes
- 自动定位兴趣区域，分块重建避免模块膨胀
- 通过共线性采样优化曲面连续性，减少表面伪影
- 融合几何相关颜色，有效抑制光照和视角影响
许多真实世界的3D重建应用需要在复杂、无界场景中实现逼真的视觉效果与度量准确性，而当前基于神经辐射场（NeRF）的管线在挑战性光照和不完整采集条件下仅部分满足需求。本研究将NeRF扩展至多兴趣区域的无界场景重建，提升对光照与位姿变化的鲁棒性，并保证适用于数字孪生的度量精度。提出的方法包括：(i) 自动化局部区域定位与重建，无缝聚焦重点区域且不增加子模块；(ii) 强制共线性射线采样，以学习平滑的平面与曲面；(iii) 深度局部邻域点提取，抑制表面伪影；(iv) 几何相关的颜色聚合，缓解光照与位姿引起的偏差。实验表明，该方法在性能上优于基线NeRF模型及经典的结构光从运动（SfM）-多视图立体（MVS）方案。
原文摘要 · Abstract (English)
Many real-world 3D reconstruction applications demand photorealism and metric accuracy across unbounded, complex scenes with challenging lighting and imperfect captures that current Neural Radiance Field (NeRF) pipelines only partly satisfy. This study adapts NeRF-based 3D reconstruction to multi-region of interest unbounded scenes to improve robustness to lighting and pose variation while enforcing metric accuracy suitable for digital-twin applications. Our approach introduces (i) automated local region localization/detection and reconstruction to seamlessly prioritize areas of interest without proliferating submodules, (ii) collinearity-enforcing ray sampling to learn smooth planar and curved surfaces, (iii) depth-localized neighborhood point extraction to suppress surface artifacts, and (iv) geometry-relevant color aggregation to mitigate lighting- and pose-caused variations. Results indicate superior performance of the proposed pipeline over the baseline NeRF models and established Structure from Motion (SfM) - Multi-View Stereo (MVS) solutions.
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