GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion

• URL: http://arxiv.org/abs/2603.21487v1
• Date: Mon, 23 Mar 2026 02:21:22 GMT
• Title: GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion
• Abstract summary: We present emphGaussianSSC, a two-stage, grid-native and triplane-guided approach to semantic scene completion (SSC)<n>We introduce emphGaussian Anchoring, a sub-pixel, Gaussian-weighted image aggregation over fused FPN features that tightens voxel--image alignment and improves monocular occupancy estimation.<n>We further convert point-like voxel features into a learned per-voxel Gaussian field and refine triplane features via a triplane-aligned emphGaussian--Triplane Refinement
• Score: 42.55859426003959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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