在相机位姿有噪声时，仍能重建可编辑的高质量3D网格。
RePose-NeRF: Robust Radiance Fields for Mesh Reconstruction under Noisy Camera Poses
- 联合优化相机位姿与隐式场景表示，提升鲁棒性。
- 在标准数据集上实现高精度重建，抗位姿误差。
- 输出兼容主流3D工具的网格，适合机器人应用。
从多视角图像进行精确3D重建对导航、操作和环境理解等下游机器人任务至关重要。然而，在真实场景中即使已知标定参数，获取精确相机位姿仍具挑战性，这限制了依赖准确外参的现有NeRF方法的实用性。此外，其隐式体素表示与广泛使用的多边形网格差异大，导致在标准3D软件中渲染和操作效率低下。本文提出一种鲁棒框架，直接从带有噪声外参的多视图图像重建高质量、可编辑的3D网格。该方法联合优化相机位姿，并学习捕捉精细几何细节与逼真外观的隐式场景表示。生成的网格兼容常见3D图形与机器人工具，支持高效下游应用。在标准基准上的实验表明，该方法在位姿不确定性下仍能实现准确且鲁棒的3D重建，弥合了神经隐式表示与实际机器人应用之间的差距。
原文摘要 · Abstract (English)
Accurate 3D reconstruction from multi-view images is essential for downstream robotic tasks such as navigation, manipulation, and environment understanding. However, obtaining precise camera poses in real-world settings remains challenging, even when calibration parameters are known. This limits the practicality of existing NeRF-based methods that rely heavily on accurate extrinsic estimates. Furthermore, their implicit volumetric representations differ significantly from the widely adopted polygonal meshes, making rendering and manipulation inefficient in standard 3D software. In this work, we propose a robust framework that reconstructs high-quality, editable 3D meshes directly from multi-view images with noisy extrinsic parameters. Our approach jointly refines camera poses while learning an implicit scene representation that captures fine geometric detail and photorealistic appearance. The resulting meshes are compatible with common 3D graphics and robotics tools, enabling efficient downstream use. Experiments on standard benchmarks demonstrate that our method achieves accurate and robust 3D reconstruction under pose uncertainty, bridging the gap between neural implicit representations and practical robotic applications.
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