Introduction to 3D Reconstruction Technology of UAV Image Processing Technology

First, briefly introduce the process of three-dimensional reconstruction (SFM).

1. Photo shooting

If you want to align with the map, the EXIF field of the collected image file needs to have GPS information or add the corresponding ground control point

2. Extract feature points

Feature points with certain invariance are extracted from photos. The feature points that can be found in multiple photos will be used for subsequent image matching and global model generation.

The commonly used feature point extraction algorithms include sift, surf, orb, etc.

3. Feature matching

Pairwise matching can be performed, which will lead to the time complexity of N2.

It can also be matched based on timing information or GPS position, which will significantly reduce the time consumption when there are many photos, but may lead to error accumulation.

Photos that successfully match enough feature points will be considered as adjacent. A set of relative position relationships can be calculated between each pair of successfully matched images to represent how the camera needs to move from the shooting position of one image to the relative position of another image. Such motion can usually be expressed as a three-dimensional rotation matrix R and a displacement vector t. The rotation matrix can also be compactly represented by the unit quaternion.

4. Optimization process

Theoretically, if the matching relationship is a chain, the position of each point can be calculated directly. However, if the matching between photos is a "net" (a connected undirected graph in the data structure), the solution result of the camera position corresponding to each photo is not unique. Although the real camera position is unique, there will be errors due to multiple observations of the same camera position. Generally, it is assumed that the error term of the unknown distribution obeys the Gaussian distribution (the information entropy of the Gaussian distribution is the largest under the same variance), so the constructed three-dimensional point reduction non overlapping loss functions are all about the square of the distance. At this time, the optimization is similar to the structure of the spring net (the energy is proportional to the square of the error). If there is external position information, it can be understood that the spring net is fixedly connected to the building, so the optimal position is determined; If not, only the position between the "springs" has the optimal solution, and where the "spring net" is can be explained arbitrarily, which has nothing to do with the optimization process.

The method to solve this optimization problem is to establish the optimization graph and construct the corresponding Jacobian matrix J and Hessian matrix H. Perform Gauss Newton or Levenberg – Marquardt descent. Here are some mathematical techniques such as turning a redundant rotating matrix (Lie group) into a non redundant vector (Lie algebra), but I won't introduce them more.

After several drops, a result close to the optimal solution is generally obtained.

5. Point cloud generation

After optimization, the position of each photo camera and the spatial position corresponding to the feature point have been determined (note here that if the EXIF information of the photo does not have GPS position and GCP, the size of the object in the image is unknown. At this time, Se (3) transform degenerates into sim (3) transform). Sparse point clouds can be reconstructed, and the position of feature points can be restored to 3D space through triangulation.

6. Point cloud densification and filtering

Through the coarse matching and coarse point cloud in the previous step, refine the part step by step, extract more features (not necessarily feature points, but also regional blocks), and match to obtain a more dense point cloud.

Generally, there are only dozens to hundreds of sift / surf feature points in an image. This matching is very robust, but obviously even if all these points are matched, it is not enough to restore any valuable graphics. This requires the densification of the point cloud and the gradual refinement of the previous camera position and attitude information (hereinafter referred to as pose) to restore a more dense point cloud.

Because it is local matching, even if the particularly robust algorithms such as sift and surf are not used, the impact of false matching is small. Patchmatching is generally used to match one area at a time. After this step, many three-dimensional vertices will be generated on the surface of the model. These vertices will appear in the final 3D model.

In the process of feature point matching and point cloud densification, there will be many miscellaneous points that have nothing to do with the local model. These points are distributed at various positions in the three-dimensional space. If they are not removed, it will seriously affect the generation of rear patches (many large and strange shapes will be generated, so that the model cannot be viewed). Therefore, some filtering algorithms need to be used to filter out obviously wrong points. (for example, one or more points far away from the main building, isolated in the air, and points far away from other point groups in a large convex hull)

7. Triangulation generation and patch refinement

Through the above operations, we have obtained a number of three-dimensional vertices ranging from tens of thousands to tens of millions (depending on the number and resolution of images, whether the size and texture of the scene are changeable, etc.). At this time, through Delaunay triangulation, a group of uniform triangles can be obtained, which will contain each vertex in the 3D graph. At this time, the surface structure of the scene will be clearly visible, but these faces have no color, only the vertices have color.

At this time, the generated patch may be rough and contain more stray parts that do not belong to the object. Some refinement algorithms can be used to refine these patches to obtain a more reasonable model.

8. Fill texture

Each triangulation will correspond to a part of the original image. Cut these parts and paste them into the corresponding small triangles to obtain a more realistic three-dimensional model with color and texture. So far, the whole process of 3D restoration and reconstruction has been completed.

Introduction to 3D Reconstruction Technology of UAV Image Processing Technology 1

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