Abstract
Automatic 3D reconstruction of indoor spaces from 2D floor plans requires high-precision semantic segmentation of structural elements, particularly walls. However, existing methods optimized for standard metrics often struggle to detect thin structural components and yield masks with irregular boundaries, lacking the geometric precision required for subsequent vectorization. To address this issue, we introduce MitUNet, a hybrid neural network architecture specifically designed for wall segmentation tasks in the context of 3D modeling. In MitUNet, we utilize a hierarchical Mix-Transformer encoder to capture global context and a U-Net decoder enhanced with scSE attention blocks for precise boundary recovery. Furthermore, we propose an optimization strategy based on the Tversky loss function to effectively balance precision and recall. By fine-tuning the hyperparameters of the loss function, we prioritize the suppression of false positive noise along wall boundaries while maintaining high sensitivity to thin structures. Our experiments on the public CubiCasa5k dataset and a proprietary regional dataset demonstrate that the proposed approach ensures the generation of structurally correct masks with high boundary accuracy, outperforming standard single-task models. MitUNet provides a robust tool for data preparation in automated 3D reconstruction pipelines.
Abstract (translated)
从二维平面图自动重建室内空间的三维模型需要对结构元素进行高精度语义分割，尤其是墙壁。然而，现有的针对标准指标优化的方法往往难以检测到薄型结构部件，并且生成的掩码边界不规则，缺乏后续矢量化所需的几何精确度。为了解决这个问题，我们引入了MitUNet，这是一种专门设计用于三维建模中墙壁分割任务的混合神经网络架构。 在MitUNet中，我们使用了一个分层的Mix-Transformer编码器来捕捉全局上下文，并且利用带有scSE注意力模块增强的U-Net解码器来恢复精确边界。此外，我们提出了一种基于Tversky损失函数的优化策略，以有效平衡精度和召回率。通过调整损失函数的超参数，我们在抑制沿墙壁边界出现的假阳性噪声的同时，保持对薄结构的高度敏感性。 在公共CubiCasa5k数据集和一个专有的地区数据集上的实验表明，我们提出的方法能够生成具有高边准确度且结构正确的掩码，在性能上超越了标准单一任务模型。MitUNet为自动化三维重建流水线的数据准备提供了一个稳健的工具。
URL
https://arxiv.org/abs/2512.02413
