Auto3R: Automated 3D Reconstruction and Scanning via Data-driven Uncertainty Quantification

Traditional high-quality 3D scanning and reconstruction typ-ically relies on human labor to plan the scanning procedure. With therapid development of embodied systems such as drones and robots, thereis a growing demand of performing accurate 3D scanning and recon-struction in an fully automated manner. We introduce Auto3R, a data-driven uncertainty quantification model that is designed to automatethe 3D scanning and reconstruction of scenes and objects, includingobjects with non-lambertian and specular materials. Specifically, in aprocess of iterative 3D reconstruction and scanning, Auto3R can makeefficient and accurate prediction of uncertainty distribution over poten-tial scanning viewpoints, without knowing the ground truth geometryand appearance. Through extensive experiments, Auto3R achieves supe-rior performance that outperforms the state-of-the-art methods, partic-ularly on challenging viewpoints. We also deploy Auto3R on a robotarm equipped with a camera and demonstrate that Auto3R can beused to effectively digitize real-world 3D objects and delivers ready-to-use and photorealistic digital assets. Our code is available at https://tomatoma00.github.io/auto3r.github.io/.