House type structure analysis method and device

Abstract

The invention discloses a house type structure analysis method and device. The method comprises the steps: S1, obtaining three-dimensional object point cloud data and three-dimensional air point cloud data; S2, identifying the three-dimensional object point cloud to obtain a furniture point cloud, performing two-dimensional object projection on three-dimensional object point cloud data to obtain a rotation correction matrix, and performing rotation correction on the three-dimensional object point cloud, the three-dimensional air point cloud and the furniture point cloud according to the rotation correction matrix; S3, generating an anti-interference two-dimensional overlook angle object projection and a normal vector projection by combining the corrected three-dimensional object point cloud and furniture information to which each point belongs, and respectively generating a two-dimensional room mask, two-dimensional angular points to which each room belongs, and relevance between the angular points; S4, generating local boundaries of the rooms and global boundaries of the floors according to the angular points, and performing optimization based on the angular points and the parallel distances of the boundaries; and S5, integrating furniture position and channel position information to generate a two-dimensional house type structure.

Description

technical field [0001] The invention relates to the technical fields of computer vision and machine learning, in particular to a room structure analysis method and device. Background technique [0002] With the popularization and extensive development of computer technology, people can obtain housing information more conveniently and quickly. At present, on the real estate transaction platform, each house will be linked with a corresponding floor plan, which describes the overall properties of the house. From the floor plan, you can intuitively see the layout, planning, orientation and area size of the entire house, etc. Information is an essential source of information for customers to understand houses. [0003] In order to provide customers with real housing information, most real estate trading platforms will not only show customers the house type map, but also display the detailed information corresponding to the house type map, such as including the overall detailed i...

AI Technical Summary

Problems solved by technology

[0006] However, there are some deficiencies in this technical solution: for example, when the house data is not a rough house, but has interference items such as furniture and residents, the simple two-dimensional projection data tends to become very "dirty", which greatly affects the Affects the accuracy of subsequent predictions; optimization based on energy functions is accompanied by a very strong prior, and often the structure is too simple, it is likely to connect two parallel but close walls into one, just to save one line; However, if a heuristic algorithm is used, the resources and time consumed by the calculation will be very large.

[0008] 1. The projection processing of the data is too simple: the two-dimensional image used as the input data is easily affected by other objects such as furniture, residents, temporary objects, etc. In addition to the wall projection, there are many other miscellaneous points mixed in the projection image , line, which greatly affects the accuracy and reliability of subsequent predictions;

[0009] 2. Lack of necessary preprocessing steps before data projection: 3D models based on current mainstream methods often follow their own coordinate system, rather than the world coordinate system of the scanned object in the real world, and it is impossible to guarantee the input The main wall direction in the model is parallel to the x-axis and y-axis

But such a priori not only makes the final output sometimes too simple, but also makes it difficult to predict some situations that are not pre-conceived

And the heuristic optimization method it uses will consume a lot of computing resources and time when the situation is more complicated

[0011] 4. The result is lack of information on objects in the room: In many applications based on the structure of the room, such as viewing or interior decoration, users are often more concerned about the interior furniture and other conditions in addition to the basic large room type.

while the prior art does not address this in the slightest

[0012] 5. Lack of channel information in the room type: the connection relationship between rooms and the location of the door are also very important parts of the room type

But the existing method does not produce any channel structure, even if the corner points of the channel are obtained in the detection, it is "optimized" by the energy function because it is on the same line as the corner points of the entire wall

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