3D colored object reconstruction from a single view image through diffusion

Abstract

In this paper we propose a novel 3D colored object reconstruction method from a single view image. Given a reference image, a conditional diffusion probabilistic model is built to reconstruct both a 3D point cloud shape and the corresponding color features at each point, and then images from arbitrary views can be synthesized using a volume rendering technique. The approach involves several sequential steps. First, the reference RGB image is encoded into separate shape and color latent variables. Then, a shape prediction module predicts reverse geometric noise based on the shape latent variable within the diffusion model. Next, a color prediction module predicts color features for each 3D point using information from the color latent variable. Finally, a volume rendering module transforms the generated colored point cloud into 2D image space, facilitating training based solely on a reference image. Experimental results demonstrate that the proposed method achieves competitive performance on colored 3D shape reconstruction and novel view image synthesis.