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.
Pipeline: Overview of our framework. During training, photometric stereo images are rendered using PBR with randomly varied materials, lighting, and camera poses, along with depth, normal, albedo, and lighting maps. Images are encoded as a mesh through the network. All associated maps, along with the images, are used for supervision. During inference, an optional multi-view diffusion model takes a single image as input and outputs multi-view images, which are then fed into the network for mesh prediction. Relighting and material editing functionalities are also supported.
Note: We use Zero123++ for image-to-multiview generation.
