  Recently, the emergence of diffusion models has opened up new opportunities
for single-view reconstruction. However, all the existing methods represent the
target object as a closed mesh devoid of any structural information, thus
neglecting the part-based structure, which is crucial for many downstream
applications, of the reconstructed shape. Moreover, the generated meshes
usually suffer from large noises, unsmooth surfaces, and blurry textures,
making it challenging to obtain satisfactory part segments using 3D
segmentation techniques. In this paper, we present Part123, a novel framework
for part-aware 3D reconstruction from a single-view image. We first use
diffusion models to generate multiview-consistent images from a given image,
and then leverage Segment Anything Model (SAM), which demonstrates powerful
generalization ability on arbitrary objects, to generate multiview segmentation
masks. To effectively incorporate 2D part-based information into 3D
reconstruction and handle inconsistency, we introduce contrastive learning into
a neural rendering framework to learn a part-aware feature space based on the
multiview segmentation masks. A clustering-based algorithm is also developed to
automatically derive 3D part segmentation results from the reconstructed
models. Experiments show that our method can generate 3D models with
high-quality segmented parts on various objects. Compared to existing
unstructured reconstruction methods, the part-aware 3D models from our method
benefit some important applications, including feature-preserving
reconstruction, primitive fitting, and 3D shape editing.
