

Reconfusion
Overview :
ReconFusion is a 3D reconstruction method that leverages diffusion priors to reconstruct real-world scenes from a limited number of photographs. It combines Neural Radiance Fields (NeRFs) with diffusion priors, enabling the synthesis of realistic geometry and textures at new camera poses beyond the input image set. This method is trained on diffusion priors with both limited-view and multi-view datasets, allowing it to synthesize realistic geometry and textures in unconstrained regions while preserving the appearance of the observed region. ReconFusion has been extensively evaluated on various real-world datasets, including forward and 360-degree scenes, demonstrating significant performance improvements.
Target Users :
ReconFusion is suitable for scenarios requiring 3D reconstruction from a limited number of views. It can synthesize realistic geometry and textures in unconstrained regions while preserving the appearance of the observed region.
Use Cases
Use Case 1: In medical imaging, ReconFusion can be used to reconstruct human organ models from a limited number of views.
Use Case 2: In architectural design, ReconFusion can be used to generate realistic architectural scenes from limited perspectives.
Use Case 3: In virtual reality applications, ReconFusion can be used to generate realistic virtual environments from a small number of input images.
Features
Uses NeRF to optimize the minimization of reconstruction loss and sample loss
Generates sample images using a PixelNeRF-style model
Combines noise latent variables and diffusion models to generate decoded output samples
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