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/ .

翻译：从单目图像序列进行流式重建仍然具有挑战性，因为现有方法通常要么偏向高质量渲染，要么偏向精确几何，但很少能兼顾两者。我们提出了PLANING，这是一个高效的在线重建框架，建立在一个混合表示之上，该表示将显式几何基元与神经高斯松散耦合，使得几何和外观能够以解耦的方式进行建模。这种解耦支持一种在线初始化和优化策略，该策略分离了几何和外观的更新，从而实现了结构冗余大幅减少的稳定流式重建。PLANING在稠密网格Chamfer-L2上比PGSR提升了18.52%，在PSNR上超过ARTDECO 1.31 dB，并在100秒内重建ScanNetV2场景，速度比2D高斯泼溅快5倍以上，同时达到了离线逐场景优化的质量。除了重建质量之外，PLANING的结构清晰度和计算效率使其非常适合于广泛的下游应用，例如实现大规模场景建模和为具身AI提供可用于模拟的环境。项目页面：https://city-super.github.io/PLANING/ 。