Paper · arXiv 2601.22046
PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction
Changjian Jiang, Kerui Ren, Xudong Li, Kaiwen Song, Linning Xu, Tao Lu, Junting Dong, Yu Zhang, Bo Dai, Mulin Yu
PLANING presents an efficient streaming reconstruction framework that combines explicit geometric primitives with neural Gaussians to achieve high-quality rendering and accurate geometry simultaneously through decoupled optimization.
Abstract
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. PLANING improves dense mesh Chamfer-L2 by 18.52% over PGSR, surpasses ARTDECO by 1.31 dB PSNR, and reconstructs ScanNetV2 scenes in under 100 seconds, over 5x faster than 2D Gaussian Splatting, while matching the quality of offline per-scene optimization. Beyond reconstruction quality, the structural clarity and computational efficiency of \modelname~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. Project page: https://city-super.github.io/PLANING/ .