Part123: Part-aware 3D Reconstruction from a Single-view Image
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.
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Anran Liu
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Cheng Lin
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Yuan Liu
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2024-05-28T04:22:39.242Z
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Yuan Liu
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liuyuan-pal
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Xiaoxiao Long
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2024-05-28T10:36:32.555Z
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xiaoxiaolongli
user
xiaoxiaolong
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Zhiyang Dou
admin_assigned
2024-05-28T10:36:41.632Z
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Zhiyang Dou
user
frankzydou
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Hao-Xiang Guo
claimed_verified
2025-08-14T13:43:28.647Z
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haoxiang guo
user
howardguo222
66554dce61422fc897949f71
Ping Luo
66554dce61422fc897949f72
Wenping Wang
Part123: Part-aware 3D Reconstruction from a Single-view Image
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.
Part123 framework uses diffusion models and contrastive learning within neural rendering to produce part-aware 3D reconstructions from single-view images with high-quality segmentation.
diffusion models Segment Anything Model (SAM) contrastive learning neural rendering 3D reconstruction part-wise segmentation feature-preserving reconstruction primitive fitting 3D shape editing