高斯泼溅是否再次神经化？关于学习参数化的分类法与受控研究
Is Gaussian Splatting Becoming Neural Again? A Taxonomy and Controlled Study of Learned Parameterization
- University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过分类法和受控实验表明，三维高斯泼溅中共享外观与不透明度的选择性神经化可提升重建质量，而解码几何结构无额外增益。
AI中文摘要：
三维高斯泼溅（3DGS）结合了显式基元与高效光栅化，然而近期系统越来越多地使用神经网络来生成或共享高斯参数。我们沿五个轴表征这一趋势：属性解码、空间共享、视图条件解码、拓扑生成和摊销推理。对19种代表性方法的分析表明，这些选择应对不同的局限性，不能简化为二元的神经标签。我们还在受控的mip-NeRF 360研究中隔离了三种形式的神经参数化。共享外观和不透明度可提高重建质量，而解码几何结构则无进一步增益。证据支持选择性神经化：当共享函数能够捕获可复用的相关性而不牺牲显式泼溅的局部几何自由度时，它们是有益的。
英文摘要：
Three-dimensional Gaussian Splatting (3DGS) combines explicit primitives with efficient rasterization, yet recent systems increasingly use neural networks to generate or share Gaussian parameters. We characterize this trend along five axes: attribute decoding, spatial sharing, view-conditioned decoding, topology generation, and amortized inference. An analysis of 19 representative methods shows that these choices address different limitations and cannot be reduced to a binary neural label. We also isolate three forms of neural parameterization in a controlled mip-NeRF 360 study. Sharing appearance and opacity improves reconstruction quality, while decoding geometric structure offers no further gain. The evidence favors selective neuralization: shared functions help when they capture reusable correlations without sacrificing the local geometric freedom of explicit splats.
