DySurface: 通过连接显式高斯和隐式函数实现一致的4D表面重建
DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions
AI总结：
本文提出DySurface框架，通过连接显式高斯和隐式SDF，解决动态场景中几何表面重建的连续性问题，提升表面重建质量与边界完整性。
摘要：
尽管动态场景的视图合成（NVS）取得了显著进展，但重建时间一致的几何表面仍具挑战性。NeRF和3DGS提供了强大的动态场景渲染能力；然而，仅依赖光度优化常导致几何歧义。为此，我们提出DySurface，一种新的框架，通过连接显式高斯的有效性与隐式SDF的几何保真度。我们的方法解决3DGS前向变形（canonical→dynamic）与体积SDF渲染所需后向变形（dynamic→canonical）之间的结构差异。具体而言，我们提出了VoxGS-DSDF分支，利用变形高斯构建动态稀疏体素网格，为隐式SDF场提供显式几何指导。这种显式锚定有效正则化体积渲染过程，显著提高表面重建质量，具有封闭边界和详细表示。定量和定性实验表明，DySurface在几何准确性度量上显著优于现有最佳基线，同时保持竞争性的渲染性能。
英文摘要：
While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains a challenge. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) offer powerful dynamic scene rendering capabilities; however, relying solely on photometric optimization often leads to geometric ambiguities. This results in discontinuous surfaces, severe artifacts, and broken surfaces over time. To address these limitations, we present DySurface, a novel framework that bridges the effectiveness of explicit Gaussians with the geometric fidelity of implicit Signed Distance Functions (SDFs) in dynamic scenes. Our approach tackles the structural discrepancy between the forward deformation of 3DGS ($canonical \rightarrow dynamic$) and the backward deformation required for volumetric SDF rendering ($dynamic \rightarrow canonical$). Specifically, we propose the VoxGS-DSDF branch that leverages deformed Gaussians to construct a dynamic sparse voxel grid, providing explicit geometric guidance to the implicit SDF field. This explicit anchoring effectively regularizes the volumetric rendering process, significantly improving surface reconstruction quality, with watertight boundaries and detailed representations. Quantitative and qualitative experiments demonstrate that DySurface significantly outperforms state-of-the-art baselines in geometric accuracy while maintaining competitive rendering performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
