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.

翻译：许多真实世界中的三维重建应用要求在光照条件复杂、数据采集不完美的无界场景中实现照片级真实感与度量精度，而当前的神经辐射场（NeRF）管线仅能部分满足这些需求。本研究将基于NeRF的三维重建方法适配至多感兴趣区域的无界场景，以提升对光照与位姿变化的鲁棒性，同时确保适用于数字孪生应用的度量精度。我们的方法提出：（i）自动局部区域定位/检测与重建，可在不增加子模块数量的情况下无缝优先关注兴趣区域；（ii）共线性约束的射线采样，用于学习平滑的平面与曲面；（iii）深度局部化的邻域点提取，以抑制表面伪影；（iv）几何相关的颜色聚合，以缓解光照与位姿引起的颜色变化。实验结果表明，所提出的管线在性能上优于基线NeRF模型以及成熟的运动恢复结构（SfM）与多视图立体（MVS）解决方案。