解决稀疏视角动态3D重建中的几何失真问题，提升细节还原与时空一致性。
GeoRect4D: Geometry-Compatible Generative Rectification for Dynamic Sparse-View 3D Reconstruction
- 通过闭环优化将生成修复与3D几何一致性耦合，避免结构漂移。
- 在多个数据集上实现最高重建保真度和时序一致性，浮点物消除率显著提升。
- 适合需要高精度动态场景重建的研究者或工业应用开发者。
从稀疏多视角视频重建动态3D场景极具挑战性，常导致几何坍塌、轨迹漂移和浮动物。现有方法引入生成先验以补全缺失内容，但随机2D生成与确定性3D几何间的不匹配易引发结构漂移和时序不一致。本文提出GeoRect4D，一种统一的稀疏视角动态重建框架，通过闭环优化将显式3D一致性与生成精修结合。具体而言，该框架引入退化感知反馈机制，融合基于锚点的动态3DGS基础结构与单步扩散修正器，利用结构锁定机制和时空协同注意力，在恢复高保真细节的同时保持物理合理性。此外，提出渐进式优化策略，采用随机几何净化消除浮动物，并通过生成蒸馏将纹理细节注入显式表示。大量实验表明，GeoRect4D在多个数据集上均达到最优的重建保真度、感知质量和时空一致性表现。
原文摘要 · Abstract (English)
Reconstructing dynamic 3D scenes from sparse multi-view videos is highly ill-posed, often leading to geometric collapse, trajectory drift, and floating artifacts. Recent attempts introduce generative priors to hallucinate missing content, yet naive integration frequently causes structural drift and temporal inconsistency due to the mismatch between stochastic 2D generation and deterministic 3D geometry. In this paper, we propose GeoRect4D, a novel unified framework for sparse-view dynamic reconstruction that couples explicit 3D consistency with generative refinement via a closed-loop optimization process. Specifically, GeoRect4D introduces a degradation-aware feedback mechanism that incorporates a robust anchor-based dynamic 3DGS substrate with a single-step diffusion rectifier to hallucinate high-fidelity details. This rectifier utilizes a structural locking mechanism and spatiotemporal coordinated attention, effectively preserving physical plausibility while restoring missing content. Furthermore, we present a progressive optimization strategy that employs stochastic geometric purification to eliminate floaters and generative distillation to infuse texture details into the explicit representation. Extensive experiments demonstrate that GeoRect4D achieves state-of-the-art performance in reconstruction fidelity, perceptual quality, and spatiotemporal consistency across multiple datasets. Project Page: https://mediax-sjtu.github.io/GeoRect4D
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