Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowadays. Classic solutions, such as Poisson surface reconstruction, require point normals as extra input to perform reasonable results. Modern transformer-based methods can work without normals, while the results are less fine-grained due to limited encoding performance in local fusion from discrete points. We introduce a novel normalized matrix attention transformer (Tensorformer) to perform high-quality reconstruction. The proposed matrix attention allows for simultaneous point-wise and channel-wise message passing, while the previous vector attention loses neighbor point information across different channels. It brings more degree of freedom in feature learning and thus facilitates better modeling of local geometries. Our method achieves state-of-the-art on two commonly used datasets, ShapeNetCore and ABC, and attains 4% improvements on IOU on ShapeNet. Our implementation will be released upon acceptance.

翻译：从原始点云进行曲面重构在计算机图形学界已有数十年的研究历史，如今建模与渲染应用对此需求极高。经典方案（如泊松曲面重构）需将点法线作为额外输入才能获得合理结果。基于Transformer的现代方法虽可无需法线，但由于离散点局部融合中的编码性能局限，其重构结果精细度不足。本文提出新型归一化矩阵注意力Transformer（Tensorformer）以实现高质量重构。所提出的矩阵注意力支持逐点与逐通道的并行消息传递，而传统向量注意力则会丢失跨通道的邻域点信息。该机制为特征学习带来更高自由度，从而促进局部几何建模效果的提升。本方法在ShapeNetCore与ABC两个通用数据集上均达到最优性能，且在ShapeNet数据集上的IOU指标提升4%。相关实现将在论文录用后公开。