Implicit neural networks have emerged as a crucial technology in 3D surface reconstruction. To reconstruct continuous surfaces from discrete point clouds, encoding the input points into regular grid features (plane or volume) has been commonly employed in existing approaches. However, these methods typically use the grid as an index for uniformly scattering point features. Compared with the irregular point features, the regular grid features may sacrifice some reconstruction details but improve efficiency. To take full advantage of these two types of features, we introduce a novel and high-efficiency attention mechanism between the grid and point features named Point-Grid Transformer (GridFormer). This mechanism treats the grid as a transfer point connecting the space and point cloud. Our method maximizes the spatial expressiveness of grid features and maintains computational efficiency. Furthermore, optimizing predictions over the entire space could potentially result in blurred boundaries. To address this issue, we further propose a boundary optimization strategy incorporating margin binary cross-entropy loss and boundary sampling. This approach enables us to achieve a more precise representation of the object structure. Our experiments validate that our method is effective and outperforms the state-of-the-art approaches under widely used benchmarks by producing more precise geometry reconstructions. The code is available at https://github.com/list17/GridFormer.

翻译：摘要：隐式神经网络已成为三维表面重建中的关键技术。为了从离散点云重建连续表面，现有方法通常将输入点编码为规则网格特征（平面或体积）。然而，这些方法通常将网格作为均匀散射点特征的索引。与不规则点特征相比，规则网格特征可能牺牲部分重建细节，但能提升效率。为充分利用这两类特征的优势，我们提出了一种新颖且高效的网格与点特征注意力机制，称为点-网格变换器（GridFormer）。该机制将网格视为连接空间与点云的传递点。我们的方法最大化了网格特征的空间表达力，同时保持了计算效率。此外，对整个空间的预测优化可能导致边界模糊。为解决此问题，我们进一步提出了一种结合边界二元交叉熵损失与边界采样的边界优化策略。该方法使我们能够更精确地表示物体结构。实验证明，我们的方法在广泛使用的基准测试下有效且优于当前最先进方法，能够生成更精确的几何重建结果。代码已开源在 https://github.com/list17/GridFormer。