While 3D Gaussian Splatting (3DGS) has recently demonstrated remarkable rendering quality and efficiency in 3D scene reconstruction, it struggles with varying lighting conditions and incidental occlusions in real-world scenarios. To accommodate varying lighting conditions, existing 3DGS extensions apply color mapping to the massive Gaussian primitives with individually optimized appearance embeddings. To handle occlusions, they predict pixel-wise uncertainties via 2D image features for occlusion capture. Nevertheless, such massive color mapping and pixel-wise uncertainty prediction strategies suffer from not only additional computational costs but also coarse-grained lighting and occlusion handling. In this work, we propose a nexus kernel-driven approach, termed NexusSplats, for efficient and finer 3D scene reconstruction under complex lighting and occlusion conditions. In particular, NexusSplats leverages a novel light decoupling strategy where appearance embeddings are optimized based on nexus kernels instead of massive Gaussian primitives, thus accelerating reconstruction speeds while ensuring local color consistency for finer textures. Additionally, a Gaussian-wise uncertainty mechanism is developed, aligning 3D structures with 2D image features for fine-grained occlusion handling. Experimental results demonstrate that NexusSplats achieves state-of-the-art rendering quality while reducing reconstruction time by up to 70.4% compared to the current best in quality.
虽然 3D Gaussian Splatting (3DGS) 在3D场景重建中展现了卓越的渲染质量和效率，但在现实场景中面对光照变化和偶然遮挡时表现不足。为适应光照变化，现有的 3DGS 扩展方法通过单独优化的外观嵌入对大量高斯基元进行颜色映射。为处理遮挡，它们基于 2D 图像特征预测像素级的不确定性。然而，这些方法的海量颜色映射和像素级不确定性预测策略不仅增加了计算成本，还在光照和遮挡处理上存在粗粒度问题。 为此，我们提出了一种基于 核驱动策略 的方法，称为 NexusSplats，以高效且精细地重建复杂光照和遮挡条件下的 3D 场景。具体而言，NexusSplats 引入了一种新颖的 光照解耦策略，通过优化基于核的外观嵌入而非大量高斯基元，显著加速重建速度，同时确保局部颜色一致性以获得更精细的纹理。此外，我们开发了一种 高斯级不确定性机制，将 3D 结构与 2D 图像特征对齐，实现精细的遮挡处理。 实验结果表明，NexusSplats 在渲染质量上达到当前最先进水平，同时相比质量最佳的现有方法，重建时间减少了 70.4%。