为3D高斯点云添加像素级不确定性预测，提升视觉可靠性。
Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
- 基于贝叶斯正则化的最小二乘优化，后处理生成视图相关不确定性。
- 不改变原模型，保持渲染质量，每像素输出不确定性值。
- 显著提升视图选择、场景变化与异常检测性能，适合自动驾驶等场景。
近期3D高斯点云技术在新视角合成中实现了逼真的视觉效果。然而，要将该技术从渲染引擎转化为自主系统和安全关键应用中的可信空间地图，不仅需要高质量渲染，还需明确表示的不确定性。本文提出一种轻量、即插即用的像素级、视图相关的不确定性预测框架。该方法采用后处理策略，将不确定性建模为对重建残差的贝叶斯正则化线性最小二乘优化。该架构无关的方法无需修改原始场景表示，即可提取每个原始体素的不确定性通道，且不影响基线视觉保真度。关键在于，提供这一可操作的可靠性信号，成功将3D高斯点云转化为可信的空间地图，并在三个关键下游感知任务中实现性能提升：主动视图选择、无姿态场景变化检测与无姿态异常检测。
原文摘要 · Abstract (English)
Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map for autonomous agents and safety-critical applications, knowing where the representation is uncertain is as important as the rendering fidelity itself. We bridge this critical gap by introducing a lightweight, plug-and-play framework for pixel-wise, view-dependent predictive uncertainty estimation. Our post-hoc method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals. This architecture-agnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation or degrading baseline visual fidelity. Crucially, we demonstrate that providing this actionable reliability signal successfully translates 3D Gaussian splatting into a trustworthy spatial map, further improving state-of-the-art performance across three critical downstream perception tasks: active view selection, pose-agnostic scene change detection, and pose-agnostic anomaly detection.
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