PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction

• URL: http://arxiv.org/abs/2504.08410v2
• Date: Mon, 14 Apr 2025 01:50:54 GMT
• Title: PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction
• Authors: Mingzhi Pei, Xu Cao, Xiangyi Wang, Heng Guo, Zhanyu Ma,
• Abstract summary: We present PMNI (Pose-free Multi-view Normal Integration), a neural surface reconstruction method.<n>By enforcing geometric constraints from surface normals and multi-view shape consistency, PMNI simultaneously recovers accurate camera poses and high-fidelity surface geometry.<n> Experimental results on synthetic and real-world datasets show that our method achieves state-of-the-art performance in the reconstruction of reflective surfaces.
• Score: 20.667434274495957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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