UGOD：用于稀疏视角3D高斯泼溅的不确定性引导的不透明度与丢弃
UGOD: Uncertainty-Guided Opacity and Dropout for Sparse-View 3D Gaussian Splatting
- Manchester Metropolitan University（曼彻斯特城市大学）
- University of Surrey（萨里大学）
- Imperial College London（帝国理工学院）
- University of Exeter（埃克塞特大学）
- Southeast University（东南大学）
- Nanjing University of Science and Technology（南京理工大学）
- University of Leeds（利兹大学）
- Aston University（阿斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对稀疏视角3D高斯泼溅的过拟合问题，UGOD通过估计高斯不确定性并调节其渲染贡献，结合不透明度调制与软丢弃，提升新视角合成质量并生成更紧凑表示。
AI中文摘要：
稀疏视角下的3D高斯泼溅容易过拟合，因为有限的观测使许多高斯基元约束不足，但其贡献仍通过alpha混合累积。没有不确定性估计，渲染器无法区分不可靠基元与约束良好的基元，导致其错误贡献破坏新视角合成。我们提出UGOD，一个不确定性引导框架，为每个高斯估计与视角相关的不确定性分数，并用其调节渲染贡献。一个以高斯属性和视角方向为条件的轻量级不确定性头预测该分数，进而驱动可微的不透明度调制机制，在合成前衰减高不确定性基元。训练期间，一个分离的软丢弃分支应用不确定性控制的连续保留掩码，以阻止模型依赖约束较差的高斯，从而减少过拟合。关键在于，分离不确定性分数可防止该随机正则化器的梯度偏置或破坏不确定性预测。在Mip-NeRF 360和LLFF上的实验表明，UGOD改善了稀疏视角新视角合成，并产生比对比方法更紧凑的高斯表示。这些结果证明高斯不确定性为稀疏视角重建提供了有效的渲染时控制。
英文摘要：
Sparse-view 3D Gaussian Splatting is prone to overfitting because limited observations leave many Gaussian primitives weakly constrained, yet their contributions are still accumulated through alpha blending. Without uncertainty estimation, the renderer cannot distinguish unreliable primitives from well-constrained ones, allowing their erroneous contributions to corrupt novel-view synthesis. We introduce UGOD, an uncertainty-guided framework that estimates a view-dependent uncertainty score for each Gaussian and uses it to regulate its rendering contribution. A lightweight uncertainty head conditioned on Gaussian attributes and viewing direction predicts this score, which then drives a differentiable opacity-modulation mechanism that attenuates high-uncertainty primitives before compositing. During training, a detached soft-dropout branch applies an uncertainty-controlled continuous keep mask to discourage the model from relying on poorly constrained Gaussians and thereby reduce overfitting. Crucially, detaching the uncertainty score prevents gradients from this stochastic regulariser from biasing or collapsing the uncertainty prediction. Experiments on Mip-NeRF~360 and LLFF show that UGOD improves sparse-view novel-view synthesis while producing more compact Gaussian representations than the compared methods. These results demonstrate that Gaussian uncertainty provides an effective rendering-time control for sparse-view reconstruction.
