单张图像三维重建One-2-3-45: Any Single Image to 3D Mesh in 45Seconds without Per-Shape Optimization

Minghua Liu1∗Chao Xu2∗Haian Jin3,4∗Linghao Chen1,4∗Mukund Varma T5Zexiang Xu6Hao Su1。One-2-3-45: Any Single Image to 3D Mesh in 45
Seconds without Per-Shape Optimization，Project Website: http://one-2-3-45.com

Abstract

Single image 3D reconstruction is an important but challenging task that requires
extensive knowledge of our natural world. Many existing methods solve this
problem by optimizing a neural radiance field under the guidance of 2D diffusion
models but suffer from lengthy optimization time, 3D inconsistency results, and
poor geometry. In this work, we propose a novel method that takes a single image
of any object as input and generates a full 360-degree 3D textured mesh in a single
feed-forward pass. Given a single image, we first use a view-conditioned 2D
diffusion model, Zero123, to generate multi-view images for the input view, and
then aim to lift them up to 3D space. Since traditional reconstruction methods
struggle with inconsistent multi-view predictions, we build our 3D reconstruction
module upon an SDF-based generalizable neural surface reconstruction method
and propose several critical training strategies to enable the reconstruction of 360-
degree meshes. Without costly optimizations, our method reconstructs 3D shapes
in significantly less time than existing methods. Moreover, our