用稀疏特征体表示，实现高保真且通用的神经表面重建。
High-Fidelity and Generalizable Neural Surface Reconstruction with Sparse Feature Volumes
- 采用两阶段法：先预测体素占用率，再仅在高占用区域计算特征与渲染
- 存储量减少50倍以上，可在普通硬件上实现512³分辨率重建
- 适合需要高精度、低资源消耗的3D场景重建任务
通用神经表面重建技术无需逐场景优化即可从少量图像中重建三维场景，其中密集3D特征体作为全局场景表示已被证明有效。然而，密集表示难以扩展至更高体素分辨率，严重限制了重建质量。为此，本文提出一种稀疏表示方法，在标准硬件上实现更高的分辨率并极大提升内存效率。通过两阶段流程：首先训练网络从带姿态的图像和深度图预测体素占用率；随后仅在占用率较高的体素中计算特征并进行体素渲染。为支持该稀疏表示，我们设计了高效的采样、特征聚合与查询算法，突破了现有工作对密集体素的依赖。在公开数据集上的实验表明，本方法将存储需求降低超过50倍，同时在类似硬件条件下实现512³分辨率重建（典型为128³），且重建精度优于当前最先进方法。
原文摘要 · Abstract (English)
Generalizable neural surface reconstruction has become a compelling technique to reconstruct from few images without per-scene optimization, where dense 3D feature volume has proven effective as a global representation of scenes. However, the dense representation does not scale well to increasing voxel resolutions, severely limiting the reconstruction quality. We thus present a sparse representation method, that maximizes memory efficiency and enables significantly higher resolution reconstructions on standard hardware. We implement this through a two-stage approach: First training a network to predict voxel occupancies from posed images and associated depth maps, then computing features and performing volume rendering only in voxels with sufficiently high occupancy estimates. To support this sparse representation, we developed custom algorithms for efficient sampling, feature aggregation, and querying from sparse volumes-overcoming the dense-volume assumptions inherent in existing works. Experiments on public datasets demonstrate that our approach reduces storage requirements by more than 50 times without performance degradation, enabling reconstructions at $512^3$ resolution compared to the typical $128^3$ on similar hardware, and achieving superior reconstruction accuracy over current state-of-the-art methods.
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