How can diffusion models be combined with 3D reconstruction to achieve better performance?
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Diffusion models can be combined with 3D reconstruction to achieve better performance by using them as priors for reconstruction tasks. By leveraging diffusion models, it is possible to address inverse problems in an unsupervised way by constraining the sampling process based on a conditioning input. This approach allows for the inpainting of self-occluded areas and unknown components, resulting in more accurate and consistent reconstruction. Additionally, diffusion models can be trained on high-quality datasets to learn the structural distribution of the target object, enabling the generation of high-resolution images even in the presence of unseen factors. These models can be used to condition the generation process and produce high-resolution reconstruction results without the need for re-training.
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| Papers (5) | Insight |
|---|---|
1 Citations | Diffusion models are used as a prior for 3D facial BRDF reconstruction by sampling from the model to inpaint self-occluded areas and unknown reflectance components, resulting in more accurate reflectance estimation. |
10 May 2023 | Diffusion models are used as a prior for 3D facial BRDF reconstruction by sampling from the model to inpaint self-occluded areas and unknown reflectance components, resulting in more accurate reflectance estimation. |
21 Jun 2023 | Diffusion models can be combined with 3D reconstruction by pre-training the diffusion model to learn the structural distribution of biological tissue from lateral microscopic images, and then using low-axial-resolution microscopy images to condition the generation process and generate high-axial-resolution reconstruction results. This allows for isotropic reconstruction without requiring re-training. |
| Diffusion models can be combined with 3D reconstruction by pre-training the diffusion model to learn the structural distribution of biological tissue from lateral microscopic images, and then using low-axial-resolution microscopy images to condition the generation process and generate high-axial-resolution reconstruction results. This allows for isotropic reconstruction without requiring re-training. | |
| The paper proposes using Latent Diffusion Models (LDMs) to scale Denoising Diffusion Probabilistic Models (DDPMs) to high-resolution 3D medical data, resulting in better performance for out-of-distribution (OOD) detection. |
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