We propose DiffuStereo, a novel system using only sparse cameras (8 in this
work) for high-quality 3D human reconstruction. At its core is a novel
diffusion-based stereo module, which introduces diffusion models, a type of
powerful generative models, into the iterative stereo matching network. To this
end, we design a new diffusion kernel and additional stereo constraints to
facilitate stereo matching and depth estimation in the network. We further
present a multi-level stereo network architecture to handle high-resolution (up
to 4k) inputs without requiring unaffordable memory footprint. Given a set of
sparse-view color images of a human, the proposed multi-level diffusion-based
stereo network can produce highly accurate depth maps, which are then converted
into a high-quality 3D human model through an efficient multi-view fusion
strategy. Overall, our method enables automatic reconstruction of human models
with quality on par to high-end dense-view camera rigs, and this is achieved
using a much more light-weight hardware setup. Experiments show that our method
outperforms state-of-the-art methods by a large margin both qualitatively and
quantitatively.

提出了一种使用稀疏相机实现高质量 3D 人体重建的新系统 DiffuStereo，其核心是使用扩散模型将扩散模型引入到迭代立体匹配网络中，以实现立体匹配和深度估计。通过多级扩散模型立体网络结构处理高分辨率输入，该方法可以自动重建具有与高端重度视角摄像机组相当质量的人体模型。实验表明，该方法在定性和定量上均优于现有方法。

DiffuStereo: 基于扩散的稀疏摄像机双目立体视觉的高质量人体重建

DiffuStereo: High Quality Human Reconstruction via Diffusion-based  Stereo Using Sparse Cameras

Monocular depth estimation aims at estimating a pixelwise depth map for a
single image, which has wide applications in scene understanding and autonomous
driving. Existing supervised and unsupervised methods face great challenges.
Supervised methods require large amounts of depth measurement data, which are
generally difficult to obtain, while unsupervised methods are usually limited
in estimation accuracy. Synthetic data generated by graphics engines provide a
possible solution for collecting large amounts of depth data. However, the
large domain gaps between synthetic and realistic data make directly training
with them challenging. In this paper, we propose to use the stereo matching
network as a proxy to learn depth from synthetic data and use predicted stereo
disparity maps for supervising the monocular depth estimation network.
Cross-domain synthetic data could be fully utilized in this novel framework.
Different strategies are proposed to ensure learned depth perception capability
well transferred across different domains. Our extensive experiments show
state-of-the-art results of monocular depth estimation on KITTI dataset.

本文提出了一种使用立体匹配网络作为代理从合成数据中学习深度并使用预测的立体视差图来监督单目深度估计网络的方法，并提出了不同的策略以确保学习到的深度感知能力在不同领域之间得到很好的转移。通过大量实验证明了该方法在 KITTI 数据集上具有最先进的单目深度估计结果。