PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction

Streaming reconstruction from monocular image sequences remains challenging, as existing methods typically favor either high-quality rendering or accurate geometry, but rarely both. We present PLANING, an efficient on-the-fly reconstruction framework built on a hybrid representation that loosely couples explicit geometric primitives with neural Gaussians, enabling geometry and appearance to be modeled in a decoupled manner. This decoupling supports an online initialization and optimization strategy that separates geometry and appearance updates, yielding stable streaming reconstruction with substantially reduced structural redundancy. Despite operating online, PLANING improves dense mesh Chamfer-L2 by 18.52% over the offline baseline PGSR, surpasses the streaming baseline ARTDECO by 1.31 dB in PSNR, and reconstructs ScanNetV2 scenes in under 100 seconds, over 5x faster than offline 2D Gaussian Splatting while matching per-scene optimization quality. Beyond reconstruction quality, the structural clarity and computational efficiency of PLANING make it well suited for a broad range of downstream applications, such as enabling large-scale scene modeling and simulation-ready environments for embodied AI.