论文阅读：基于网格的高精度实时三维重建，ScalableFusion: High-resolution Mesh-based Real-time 3D Reconstruction

最新推荐文章于 2025-10-22 09:10:20 发布

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最新推荐文章于 2025-10-22 09:10:20 发布 · 4.5k 阅读

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介绍ScalableFusion，一种实时三维重建方法，利用mesh提高重建精度，优化颜色分辨率，支持大规模场景，改进内存管理。与ElasticFusion等方法对比，展示其在细节、纹理质量和内存效率方面的优势。

图1:三个苹果的网格结果(从图2中放大)。由于不依赖过多的重叠的surfels来实现一个紧密的表面模型，网格（mesh）重建可以更精确、更有效地描述表面。

译者按：

本文为了充分利用RGB-D传感器中比depth分辨率更高的color信息，用mesh做reconstruction，更详细地，其借鉴了ElasticFusion中的权重方法，并且利用了GPU、CPU内存的协同来实现较大规模的重建，可以对surfel-based，TSDF-based和mesh-based的方法获得一定的了解。

摘要：

Dense 3D reconstructions generate globally consisent data of the environment suitable for many robot applications. Current RGB-D based reconstructions, however, only maintain the color resolution equal to the depth resolution of the used sensor. This firmly limits the precision and realism of the generated reconstructions. In this paper we present a real-time approach for creating and maintaining a surface reconstruction in as high as possible geometrical fidelity with full sensor resolution for its colorization (or surface texture). A multi-scale memory management process and a Level of Detail scheme enable equally detailed reconstructions to be generated at small scales, such as objects, as well as large scales, such as rooms or buildings. We showcase the benefit of this novel pipeline with a PrimeSense RGB-D camera as well as combining