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.

翻译：尽管三维高斯溅射（3DGS）近期在三维场景重建中展现出卓越的渲染质量与效率，但其在真实场景中难以应对动态光照条件与偶然遮挡。为适应变化的光照，现有3DGS扩展方法通过对海量高斯基元进行色彩映射并单独优化其外观嵌入。为处理遮挡，它们通过二维图像特征预测像素级不确定性以捕捉遮挡信息。然而，这类大规模色彩映射与像素级不确定性预测策略不仅带来额外计算开销，其光照与遮挡处理也较为粗糙。本研究提出一种基于关联核的驱动方法——NexusSplats，旨在复杂光照与遮挡条件下实现高效且精细的三维场景重建。具体而言，NexusSplats采用创新的光照解耦策略：外观嵌入基于关联核而非海量高斯基元进行优化，从而在加速重建速度的同时，通过确保局部色彩一致性实现更精细的纹理重建。此外，本研究开发了高斯级不确定性机制，将三维结构与二维图像特征对齐以实现细粒度遮挡处理。实验结果表明，NexusSplats在达到最先进渲染质量的同时，较当前最优质量方法最高可减少70.4%的重建时间。