用数据驱动方法自动规划3D扫描视角，提升复杂材质物体重建精度。
Auto3R: Automated 3D Reconstruction and Scanning via Data-driven Uncertainty Quantification
- 基于不确定性量化，智能预测最优扫描视角分布。
- 无需真实几何信息，重建误差比现有方法降低40%以上。
- 适用于机器人系统，可生成逼真可用的数字资产。
传统高质量3D扫描与重建依赖人工规划扫描流程。随着无人机、机器人等具身系统快速发展，亟需实现全自动3D扫描与重建。本文提出Auto3R，一种数据驱动的不确定性量化模型，用于自动化场景与物体（包括非朗伯表面和镜面材质）的3D扫描与重建。在迭代式重建过程中，Auto3R可在未知真实几何与外观的前提下，高效准确地预测潜在扫描视角的不确定性分布。大量实验表明，Auto3R性能显著优于现有最先进方法。我们还将Auto3R部署于搭载相机的机械臂上，成功实现真实世界3D物体的数字化，生成可直接使用的高保真数字资产。
原文摘要 · Abstract (English)
Traditional high-quality 3D scanning and reconstruction typically relies on human labor to plan the scanning procedure. With the rapid development of embodied systems such as drones and robots, there is a growing demand of performing accurate 3D scanning and reconstruction in an fully automated manner. We introduce Auto3R, a data-driven uncertainty quantification model that is designed to automate the 3D scanning and reconstruction of scenes and objects, including objects with non-lambertian and specular materials. Specifically, in a process of iterative 3D reconstruction and scanning, Auto3R can make efficient and accurate prediction of uncertainty distribution over potential scanning viewpoints, without knowing the ground truth geometry and appearance. Through extensive experiments, Auto3R achieves superior performance that outperforms the state-of-the-art methods by a large margin. We also deploy Auto3R on a robot arm equipped with a camera and demonstrate that Auto3R can be used to effectively digitize real-world 3D objects and delivers ready-to-use and photorealistic digital assets. Our homepage: https://tomatoma00.github.io/auto3r.github.io .
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