无需初始相机位姿，用法向量图重建反光无纹理表面
PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction
- 用表面法向量替代图像，约束神经SDF优化
- 在无可靠初始位姿下仍实现高保真三维重建
- 适合反光或无纹理物体的3D重建任务
反光和无纹理表面在多视角三维重建中仍是难题，因缺乏可靠的跨视角视觉特征导致相机位姿校准与形状重建失败。为此，我们提出PMNI（Pose-free Multi-view Normal Integration），一种基于表面法向量图的神经表面重建方法。通过在神经有符号距离函数（SDF）优化框架中引入法向量的几何约束与多视角形状一致性，PMNI能同时恢复精确的相机位姿与高保真表面几何。在合成与真实数据集上的实验表明，该方法在无可靠初始位姿条件下，仍可实现反射表面重建的最先进性能。
原文摘要 · Abstract (English)
Reflective and textureless surfaces remain a challenge in multi-view 3D reconstruction. Both camera pose calibration and shape reconstruction often fail due to insufficient or unreliable cross-view visual features. To address these issues, we present PMNI (Pose-free Multi-view Normal Integration), a neural surface reconstruction method that incorporates rich geometric information by leveraging surface normal maps instead of RGB images. By enforcing geometric constraints from surface normals and multi-view shape consistency within a neural signed distance function (SDF) optimization framework, PMNI simultaneously recovers accurate camera poses and high-fidelity surface geometry. Experimental results on synthetic and real-world datasets show that our method achieves state-of-the-art performance in the reconstruction of reflective surfaces, even without reliable initial camera poses.
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