We introduce a new approach for reconstruction and novel-view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene representation, which implicitly encodes a point cloud in a continuous octree-based probability field and a multi-resolution hash grid. In doing so, we combine the benefits of both worlds by retaining favorable behavior during optimization: Our novel implicit point cloud representation and differentiable bilinear rasterizer enable fast rendering while preserving fine geometric detail without depending on initial priors like structure-from-motion point clouds. Our method achieves state-of-the-art image quality on several common benchmark datasets. Furthermore, we achieve fast inference at interactive frame rates, and can extract explicit point clouds to further enhance performance.

翻译：我们提出了一种用于无界真实场景重建与新颖视角合成的新方法。与以往采用体积场、网格模型或离散点云代理的方法不同，我们提出了一种混合场景表征，通过连续的八叉树概率场和多分辨率哈希网格隐式编码点云。通过这种方式，我们融合了两种表征的优势，在优化过程中保持有利特性：我们新颖的隐式点云表征与可微双线性光栅化器能够在保持精细几何细节的同时实现快速渲染，且无需依赖诸如运动恢复结构点云等初始先验。我们的方法在多个通用基准数据集上实现了最优图像质量。此外，我们能够以交互式帧率进行快速推理，并可提取显式点云以进一步提升性能。