Abstract
Despite of the promising results on shape and color recovery using self-supervision, the multi-layer perceptrons-based methods usually costs hours to train the deep neural network due to the implicit surface representation. Moreover, it is quite computational intensive to render a single image, since a forward network inference is required for each pixel. To tackle these challenges, in this paper, we propose an efficient coarse-to-fine approach to recover the textured mesh from multi-view images. Specifically, we take advantage of a differentiable Poisson Solver to represent the shape, which is able to produce topology-agnostic and watertight surfaces. To account for the depth information, we optimize the shape geometry by minimizing the difference between the rendered mesh with the depth predicted by the learning-based multi-view stereo algorithm. In contrast to the implicit neural representation on shape and color, we introduce a physically based inverse rendering scheme to jointly estimate the lighting and reflectance of the objects, which is able to render the high resolution image at real-time. Additionally, we fine-tune the extracted mesh by inverse rendering to obtain the mesh with fine details and high fidelity image. We have conducted the extensive experiments on several multi-view stereo datasets, whose promising results demonstrate the efficacy of our proposed approach. We will make our full implementation publicly available.
Abstract (translated)
URL
https://arxiv.org/abs/2205.12468
