根据人眼感知优化高斯分布，提升3D重建质量与效率
Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
- 引入感知敏感度模型，动态调整高斯点分布
- 在大场景上实现更优的重建质量与运行效率
- 适合需要高质量3D重建的视觉应用开发者
3D高斯泼溅（3DGS）已成为新视角合成的强大技术。然而，现有方法难以根据场景特征自适应地优化高斯原语分布，导致重建质量与效率难以平衡。受人类感知启发，我们提出感知适应性稠密化框架（Perceptual-GS），将感知敏感度融入3DGS训练过程以解决该问题。首先构建一种考虑人眼视觉敏感度的表示，同时控制高斯点数量；在此基础上，设计感知敏感度自适应分布策略，将更高精度的高斯粒度分配至视觉关键区域，从而提升重建质量与鲁棒性。在多个数据集上的广泛评估，包括用于大规模场景的BungeeNeRF，结果表明Perceptual-GS在重建质量、效率和鲁棒性方面均达到当前最优水平。代码已公开于：https://github.com/eezkni/Perceptual-GS
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training process to address this challenge. We first introduce a perception-aware representation that models human visual sensitivity while constraining the number of Gaussian primitives. Building on this foundation, we develop a perceptual sensitivity-adaptive distribution to allocate finer Gaussian granularity to visually critical regions, enhancing reconstruction quality and robustness. Extensive evaluations on multiple datasets, including BungeeNeRF for large-scale scenes, demonstrate that Perceptual-GS achieves state-of-the-art performance in reconstruction quality, efficiency, and robustness. The code is publicly available at: https://github.com/eezkni/Perceptual-GS
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