Get 3D object shape using Bayesian inference and bottom-up and top-down diffusion with BDM
Get 3D object shape using Bayesian inference and bottom-up and top-down diffusion with BDM
Bayesian Diffusion Models for 3D Shape Reconstruction
arXiv paper abstract https://arxiv.org/abs/2403.06973
arXiv PDF paper https://arxiv.org/pdf/2403.06973.pdf
... present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes.
... show the effectiveness of BDM on the 3D shape reconstruction task.
... BDM brings in rich prior information from standalone labels (e.g. point clouds) to improve the bottom-up 3D reconstruction.
As opposed to the standard Bayesian frameworks where explicit prior and likelihood are required for the inference, BDM performs seamless information fusion via coupled diffusion processes with learned gradient computation networks.
... BDM ... capability to engage the active and effective information exchange and fusion of the top-down and bottom-up processes where each itself is a diffusion process.
... demonstrate state-of-the-art results on both synthetic and real-world benchmarks for 3D shape reconstruction.
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