Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving

Reconstructing 3D scene structures from sparse, low-overlapobservations remains a fundamental challenge in autonomous driving.Recent state-of-the-art frameworks achieve promising results by incor-porating voxel-based Gaussians, but incur substantial computational re-dundancy due to a uniform volumetric processing strategy. To bridgethe gap between the efficiency of pixel-based Gaussian methods and thestructural completeness of voxel-based Gaussian approaches, we proposeFocusGS, a simple yet effective framework that shifts the paradigmfrom global densification to targeted structural completion. Our centralinsight is that structural completion should be decoupled from determin-istic regions, with computation concentrated exclusively on areas exhibit-ing geometric ambiguity. Specifically, FocusGS addresses the localizationchallenge by deriving a 3D Geometric Ambiguity Manifold to accuratelyisolate localized areas prone to occlusion and high geometric uncertainty.To overcome the subsequent manifold completion challenge, we design alightweight targeted structure completion module that selectively instan-tiates and optimizes continuous Gaussian queries strictly within this un-structured, sparse topological subspace. Extensive experiments demon-strate that FocusGS achieves a superior efficiency-quality trade-off, ad-vancing state-of-the-art performance on driving-centric benchmarks whilenaturally reducing the total number of Gaussians by ∼74% and decreas-ing rendering time by ∼34%.