Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field--it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth.

翻译：详细的三维重建和数字人物的照片级真实感再照明对于各种应用至关重要。为此，我们提出了一种新颖的稀疏视角三维人体重建框架，该框架将占有场和反照率场与额外的可见度场紧密融合——它不仅解决了多视图特征聚合中的遮挡歧义，还可用于评估自阴影再照明的光衰减。为了增强其训练的可行性和效率，我们将可见度离散化到一组固定的样本方向，并为其提供耦合的几何三维深度特征和局部二维图像特征。我们进一步提出了一种新颖的基于渲染的损失函数，即TransferLoss，以隐式强制可见度场和占有场之间的对齐，从而实现端到端的联合训练。结果和大量实验证明了该方法的有效性，它在重建精度上超越了最先进的方法，同时实现了与光线追踪真实值相当准确的再照明。