Bayesian Diffusion Models for 3D Shape Reconstruction

Yu Lei

Abstract:

We 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. We show the effectiveness of BDM on the 3D
shape reconstruction task. Compared to prototypical deep
learning data-driven approaches trained on paired (supervised) data-labels (e.g. image-point clouds) datasets, our
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. The specialty of our BDM lies in its
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. We demonstrate state-of-the-art results on both synthetic and realworld benchmarks for 3D shape reconstruction