  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 method favors better geometry, generates
more 3D consistent results, and adheres more closely to the input image. We
evaluate our approach on both synthetic data and in-the-wild images and
demonstrate its superiority in terms of both mesh quality and runtime. In
addition, our approach can seamlessly support the text-to-3D task by
integrating with off-the-shelf text-to-image diffusion models.
