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

TL;DR

PLANING introduces a hybrid streaming 3D reconstruction framework that efficiently models geometry and appearance separately, achieving high quality and speed suitable for large-scale scene applications.

Contribution

It proposes a loosely coupled explicit-implicit hybrid representation enabling stable, fast, and decoupled geometry and appearance updates in streaming 3D reconstruction.

Findings

18.52% improvement in dense mesh Chamfer-L2 over PGSR

Surpasses ARTDECO by 1.31 dB PSNR

Reconstructs ScanNetV2 scenes in under 100 seconds, over 5x faster than 2D Gaussian Splatting

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 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. Project page: https://city-super.github.io/PLANING/ .