PRM: Photometric Stereo based Large Reconstruction Model

Wenhang Ge, Jiantao Lin, Guibao Shen, Jiawei Feng, Tao Hu, Xinli Xu, Ying-Cong Chen

Keywords: 3D reconstruction, feed-fowared reconstruction model, photometric stereo

Abstract:

We propose PRM, a novel photometric stereo based large reconstruction model to reconstruct high-quality meshes with fine-grained local details.
Unlike previous large reconstruction models that prepare images under fixed and simple lighting as both input and supervision, PRM renders photometric stereo images by varying materials and lighting for the purposes, which not only improves the precise local details by providing rich photometric cues but also increases the model’s robustness to variations in the appearance of input images.
To offer enhanced flexibility of images rendering, we incorporate a real-time rendering method and mesh rasterization for online images rendering.
Moreover, in employing an explicit mesh as our 3D representation, PRM ensures the application of differentiable PBR, which supports the utilization of multiple photometric supervisions and better models the specular color for high-quality geometry optimization.
Our PRM leverages photometric stereo images to achieve high-quality reconstructions with fine-grained local details, even amidst sophisticated image appearances. Extensive experiments demonstrate that PRM significantly outperforms other models.

Primary Area: generative models

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Submission Number: 2330