Dynamic Neural Scene Reconstruction from Sparse Multi-View Observations with Geometry-Aware Depth and Correspondence Constraints
The task of reconstructing dynamic neural scenes from sparse multi-view observations represents a significant challenge in computer vision and computer graphics. Traditional neural rendering techniques require dense view sampling to synthesize high-quality novel views, which is highly impractical for real-world dynamic environments where deploying numerous synchronized cameras is prohibitively expensive and logistically complex. When constrained to sparse views, existing dynamic reconstruction frameworks typically suffer from severe overfitting, resulting in pronounced geometric distortions, floaters, and temporal inconsistencies. This paper proposes a novel framework designed to achieve high-fidelity dynamic neural scene reconstruction from highly sparse viewpoints by integrating geometry-aware depth priors and robust multi-view correspondence constraints. By leveraging monocular depth estimation aligned with sparse structural cues, the system enforces a strict geometric foundation that prevents the neural field from degenerating in unobserved regions. Furthermore, we introduce a cross-view feature correspondence mechanism that penalizes photometric and geometric divergences across temporal and spatial domains, ensuring consistency in the deformation fields used to model scene dynamics. Comprehensive evaluations demonstrate that our methodology significantly suppresses artifacts and achieves superior novel view synthesis quality compared to baseline methods. The integration of these complementary constraints effectively bridges the information gap inherent in sparse observations, offering a scalable solution for dynamic scene capture.