We present a novel method for reconstructing clothed humans from a sparse set
of, e.g., 1 to 6 RGB images. Despite impressive results from recent works
employing deep implicit representation, we revisit the volumetric approach and
demonstrate that better performance can be achieved with proper system design.
The volumetric representation offers significant advantages in leveraging 3D
spatial context through 3D convolutions, and the notorious quantization error
is largely negligible with a reasonably large yet affordable volume resolution,
e.g., 512. To handle memory and computation costs, we propose a sophisticated
coarse-to-fine strategy with voxel culling and subspace sparse convolution. Our
method starts with a discretized visual hull to compute a coarse shape and then
focuses on a narrow band nearby the coarse shape for refinement. Once the shape
is reconstructed, we adopt an image-based rendering approach, which computes
the colors of surface points by blending input images with learned weights.
Extensive experimental results show that our method significantly reduces the
mean point-to-surface (P2S) precision of state-of-the-art methods by more than
50% to achieve approximately 2mm accuracy with a 512 volume resolution.
Additionally, images rendered from our textured model achieve a higher peak
signal-to-noise ratio (PSNR) compared to state-of-the-art methods.

我们提出了一种新颖的方法，用于从少量的 RGB 图像（例如 1 到 6 张）中重建穿着衣物的人类。我们重新审视了体素方法并证明了通过适当的系统设计可以获得更好的性能。体积表示通过 3D 卷积在利用三维空间上下文方面具有明显优势，对于合理的大型但保守的体积分辨率（例如 512），明显的量化误差可以忽略不计。为了处理内存和计算成本，我们提出了一个复杂的由粗到精的策略，包括体素剔除和子空间稀疏卷积。我们的方法从离散的视觉外壳开始计算粗糙形状，然后专注于粗糙形状附近的窄带进行细化。形状重建后，我们采用基于图像的渲染方法，通过将输入图像与学习到的权重混合来计算表面点的颜色。大量的实验结果表明，我们的方法将最先进方法的平均点到表面（P2S）精度降低了 50％以上，以 512 体积分辨率实现了约 2mm 的准确度。此外，我们渲染的纹理模型与最先进方法相比具有更高的峰值信噪比（PSNR）。