Wall reconstruction method and device
A wall and algorithm technology, applied in the field of wall reconstruction methods and devices, can solve the problems of strict requirements for drawing methods and field definitions, etc.
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Embodiment 1
[0041] like figure 1 As shown, the present embodiment proposes a wall reconstruction method, the method comprising:
[0042] S101. Acquire a grid floor plan, and perform semantic segmentation on the grid floor map to obtain a semantic segmentation result.
[0043] Specifically, such as figure 2 Shown is the acquired grid floor plan. In this embodiment, a neural network structure is used to carry out semantic segmentation processing on the architectural grid floor plan, preferably using a U-shaped convolutional neural network. By cleaning and screening the training data required by the network, configuring the network learning and parameter generation, and finally generating semantic segmentation results, such as image 3 shown. Of course, other types of neural network structures based on deep learning methods can also realize pixel-level representation and learning of raster floor plans. But in terms of overall effect, it is still best to use U-shaped convolutional neur...
corresponding Embodiment 1
[0058] Corresponding to Embodiment 1, this embodiment also proposes a wall reconstruction device, which includes a processor configured with operating instructions executable by the processor to perform the following operations:
[0059] Obtain the grid floor plan;
[0060] Semantic segmentation is performed on the grid floor plan to obtain a semantic segmentation result;
[0061] Perform image post-processing on the semantic segmentation result to obtain wall nodes;
[0062] Obtaining an effective and correct connection result according to the wall nodes;
[0063] The walls are reconstructed according to the effective and correct connection results.
[0064] Specifically, this embodiment uses a neural network structure to perform semantic segmentation processing on the building grid floor plan, preferably using a U-shaped convolutional neural network. By cleaning and screening the training data required by the network, configuring the network learning and parameter generat...
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