GaussianSSC: Triplane-Guided Directional Gaussian Fields for 3D Semantic Completion
This work addresses 3D scene understanding for autonomous driving by providing incremental improvements in semantic completion accuracy.
The paper tackles 3D semantic scene completion by introducing GaussianSSC, a two-stage method that uses Gaussian fields and triplane guidance to enhance voxel-image alignment and feature refinement, resulting in improved occupancy estimation and semantic prediction on SemanticKITTI with gains such as +1.8% IoU in Stage 2.
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.