MAPo：面向高保真动态场景重建的可形变三维高斯泼溅运动感知分区
MAPo: Motion-Aware Partitioning of Deformable 3D Gaussian Splatting for High-Fidelity Dynamic Scene Reconstruction
把形变网络按运动强度做时序递归分区、为每段复制形变网络，并配跨帧一致性损失，可直接迁移到任何基于形变场的动态 3DGS/NeRF 重建中，用来缓解高动态区域的模糊与细节丢失。
原文依据 a dynamic score-based partitioning strategy that distinguishes between high- and low-dynamic 3D Gaussians
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论三维高斯泼溅以快速渲染实现高质量静态场景重建而著称，正日益被应用于多视角动态场景重建。一种常见策略是学习形变场，以建模一组规范三维高斯随时间的时序变化。然而，由于单一统一模型在表示多样运动模式时存在固有局限，这类基于形变的方法在高动态区域往往产生模糊的渲染结果并丢失精细运动细节。为解决上述问题，本文提出可形变三维高斯泼溅的运动感知分区框架 MAPo，用于高保真动态场景重建。其核心是一种基于动态得分的分区策略，用于区分高动态与低动态三维高斯。对于高动态三维高斯，本文对其进行时序递归分区，并为每个新的时序分段复制其形变网络，从而实现专门化建模以捕捉复杂的运动细节。同时，低动态三维高斯被视为静态处理，以降低计算开销。然而，针对高动态三维高斯的这种时序分区策略会在分区边界处引入跨帧的视觉不连续。为此，本文引入跨帧一致性损失，不仅保证视觉连续性，还进一步提升渲染质量。大量实验表明，MAPo 在保持相当计算开销的同时，相比基线取得了更优的渲染质量，在运动复杂或快速的区域尤为明显。
英文原文摘要 1622 字符
3D Gaussian Splatting, known for enabling high-quality static scene reconstruction with fast rendering, is increasingly being applied to multi-view dynamic scene reconstruction. A common strategy involves learning a deformation field to model the temporal changes of a canonical set of 3D Gaussians. However, these deformation-based methods often produce blurred renderings and lose fine motion details in highly dynamic regions due to the inherent limitations of a single, unified model in representing diverse motion patterns. To address these challenges, we introduce Motion-Aware Partitioning of Deformable 3D Gaussian Splatting (MAPo), a novel framework for high-fidelity dynamic scene reconstruction. Its core is a dynamic score-based partitioning strategy that distinguishes between high- and low-dynamic 3D Gaussians. For high-dynamic 3D Gaussians, we recursively partition them temporally and duplicate their deformation networks for each new temporal segment, enabling specialized modeling to capture intricate motion details. Concurrently, low-dynamic 3DGs are treated as static to reduce computational costs. However, this temporal partitioning strategy for high-dynamic 3DGs can introduce visual discontinuities across frames at the partition boundaries. To address this, we introduce a cross-frame consistency loss, which not only ensures visual continuity but also further enhances rendering quality. Extensive experiments demonstrate that MAPo achieves superior rendering quality compared to baselines while maintaining comparable computational costs, particularly in regions with complex or rapid motions.