用高斯场增强三平面表示，提升单目3D语义补全精度
GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion
- 以三平面引导高斯场，实现像素级对齐与体素特征优化
- 在SemanticKITTI上提升1.8%召回率、2.0%精确率和1.8%交并比
- 适合关注3D场景补全与高效几何建模的研究者
我们提出GaussianSSC，一种两阶段、网格原生且三平面引导的语义场景补全方法。该方法引入高斯锚定机制，在融合的FPN特征上进行亚像素级加权图像聚合，强化体素与图像对齐，提升单目占用估计性能。进一步将点状体素特征转为可学习的每体素高斯场，并通过三平面对齐的高斯-三平面精化模块，结合局部聚集（目标中心）与全局聚合（源中心），捕捉表面切向性、尺度及遮挡感知的非对称性，同时保持三平面表示的高效性。在SemanticKITTI数据集上，GaussianSSC相较于最先进基线，第一阶段占用预测提升1.0%召回率、2.0%精确率、1.8%交并比；第二阶段语义预测提升1.8%交并比和0.8%mIoU。
原文摘要 · Abstract (English)
We present \emph{GaussianSSC}, a two-stage, grid-native and triplane-guided approach to semantic scene completion (SSC) that injects the benefits of Gaussians without replacing the voxel grid or maintaining a separate Gaussian set. We introduce \emph{Gaussian Anchoring}, a sub-pixel, Gaussian-weighted image aggregation over fused FPN features that tightens voxel--image alignment and improves monocular occupancy estimation. We further convert point-like voxel features into a learned per-voxel Gaussian field and refine triplane features via a triplane-aligned \emph{Gaussian--Triplane Refinement} module that combines \emph{local gathering} (target-centric) and \emph{global aggregation} (source-centric). This directional, anisotropic support captures surface tangency, scale, and occlusion-aware asymmetry while preserving the efficiency of triplane representations. On SemanticKITTI~\cite{behley2019semantickitti}, GaussianSSC improves Stage~1 occupancy by +1.0\% Recall, +2.0\% Precision, and +1.8\% IoU over state-of-the-art baselines, and improves Stage~2 semantic prediction by +1.8\% IoU and +0.8\% mIoU.
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