滴滴和港中文最新的前馈3D重建算法UniSplat！史少帅参与~

Core Insights - The article discusses the introduction of UniSplat, a novel feed-forward framework for dynamic scene reconstruction in autonomous driving, which addresses challenges in existing methods due to sparse camera views and dynamic environments [6][44]. Group 1: Background and Challenges - Reconstructing 3D scenes from urban driving scenarios is a core capability for autonomous driving systems, supporting tasks like simulation and scene understanding [5]. - Recent advancements in 3D Gaussian splatting have shown impressive rendering efficiency and fidelity, but existing methods often assume significant overlap between input images, limiting their applicability in real-time driving scenarios [5][6]. - The challenges include maintaining a unified latent representation over time, handling partial observations and occlusions, and efficiently generating high-fidelity Gaussian bodies from sparse inputs [5][6]. Group 2: UniSplat Framework - UniSplat is designed to model dynamic scenes using a unified 3D scaffold that integrates multi-view spatial information and multi-frame temporal information [6][9]. - The framework operates in three stages: constructing a 3D scaffold from multi-view images, performing spatio-temporal fusion, and decoding the fused scaffold into Gaussian bodies [6][9]. - The dual-branch decoder strategy enhances detail retention and scene completeness by predicting Gaussian bodies from both sparse point locations and voxel centers [6][9]. Group 3: Experimental Results - Evaluations on the Waymo Open and NuScenes datasets demonstrate that UniSplat achieves state-of-the-art performance in both input view reconstruction and new view synthesis tasks [7][34]. - The model exhibits strong robustness and superior rendering quality when synthesizing views outside the original camera coverage, thanks to its temporal memory mechanism [7][34]. - Comparative results indicate that UniSplat consistently outperforms existing methods, such as MVSplat and DepthSplat, across all metrics [33][34]. Group 4: Conclusion and Future Directions - UniSplat represents a significant advancement in dynamic scene reconstruction and new view synthesis, providing a robust framework for integrating spatio-temporal information from multi-camera video [44]. - The framework's potential applications extend to dynamic scene understanding, interactive 4D content creation, and lifelong world modeling [44].