Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions

Gene Chou, Yuval Bahat, Felix Heide

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpainting, and text-to-image tasks. However, they are still in the early stages of generating complex 3D shapes. This work proposes Diffusion-SDF, a generative model for shape completion, single-view reconstruction, and reconstruction of real-scanned point clouds. We use neural signed distance functions (SDFs) as our 3D representation to parameterize the geometry of various signals (e.g., point clouds, 2D images) through neural networks. Neural SDFs are implicit functions and diffusing them amounts to learning the reversal of their neural network weights, which we solve using a custom modulation module. Extensive experiments show that our method is capable of both realistic unconditional generation and conditional generation from partial inputs. This work expands the domain of diffusion models from learning 2D, explicit representations, to 3D, implicit representations. Code is released at https://github.com/princeton-computational-imaging/Diffusion-SDF.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9798350307184
StatePublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France
Duration: Oct 2 2023Oct 6 2023

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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