Reducing computational complexity in three-dimensional modeling based on two-dimensional images

1. A computer-implemented method, comprising:

obtaining a first image of an object oriented in a first direction and a second image of the object oriented in a second direction;

determining a plurality of initial feature points of the object in the first image;

matching the first image with a first grid that includes a plurality of grid cells;

determining a plurality of feature points of the object in the first image, comprising:

determining a center point of a grid cell as a feature point of the grid cell if the grid cell does not include an initial feature point; and

filtering the plurality of initial feature points until the number of feature points is the same in each grid cell;

determining a plurality of matching feature points of the object in the second image that correspond to the plurality of feature points of the object in the first image;

calculating similarity values between the plurality of feature points and the corresponding plurality of matching feature points;

calculating depth values of the plurality of feature points;

calculating weighted depth values based on the similarity values and depth values; and

performing 3D modeling of the object based on the weighted depth values.

2. The computer-implemented method of claim 1 , wherein determining a plurality of feature points of the object in the first image further comprising:

calculating distances from the plurality of initial feature points to center points of the grid cells where the plurality of initial feature points are located; and

determining, for each grid cell that includes at least one initial feature point, an initial feature point that is closest to the center of the grid cell as the feature point.

3. The computer-implemented method of claim 2 , further comprising:

determining a plurality of pre-matching feature points in the second image that correspond to the plurality of feature points based on direction information of the object in the first image and the object in the second image; and

matching the second image with a second grid; and wherein determining a plurality of matching feature points of the object in the second image further comprises determining center points of grid cells of the second grid that include at least one pre-matching feature point as the plurality of matching feature points in the second image.

4. The computer-implemented method of claim 3 , wherein determining the similarity values between the plurality of feature points and the corresponding plurality of matching feature points further comprises:

determining color values of all pixels within the grid cells of the first grid that include at least one feature point;

determining color values of all pixels within the grid cells of the second grid that include at least one matching feature point;

calculating average color value differences between all the pixels within the grid cells of the first grid that include at least one feature point and all the pixels within the grid cells of the second grid that include at least one matching feature point; and

determining the similarity values of the plurality of feature points and the corresponding plurality of matching feature points based on the average color value differences.

5. The computer-implemented method of claim 4 , wherein a weighted depth value of a feature point is calculated as:

weighted depth value=depth value×(sum of the average color value differences of the plurality of feature points except the feature point subject to the weighted depth value calculation/sum of the average color value differences of the plurality of feature points).

6. The computer-implemented method of claim 3 , wherein the area of the grid cells of the second grid are smaller than the area of the grid cells of the first grid.

7. The computer-implemented method of claim 1 , further comprising:

filtering the plurality of feature points based on the similarity values before calculating the weighted depth values.

8. The computer-implemented method of claim 7 , wherein filtering the plurality of feature points further comprising:

determining a feature point eliminating threshold based on the similarity values; and

eliminating a feature point when its corresponding similarity value is less than the feature point eliminating threshold.

9. The computer-implemented method of claim 1 , wherein the grid cells of the first grid have triangular shape.

10. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

obtaining a first image of an object oriented in a first direction and a second image of the object oriented in a second direction;

determining a plurality of initial feature points of the object in the first image;

matching the first image with a first grid that includes a plurality of grid cells;

determining a plurality of feature points of the object in the first image, comprising:

determining a center point of a grid cell as a feature point of the grid cell if the grid cell does not include an initial feature point; and

filtering the plurality of initial feature points until the number of feature points is the same in each grid cell;

determining a plurality of matching feature points of the object in the second image that correspond to the plurality of feature points of the object in the first image;

calculating similarity values between the plurality of feature points and the corresponding plurality of matching feature points;

calculating depth values of the plurality of feature points;

calculating weighted depth values based on the similarity values and depth values; and

performing 3D modeling of the object based on the weighted depth values.

11. The non-transitory, computer-readable medium of claim 10 , wherein determining a plurality of feature points of the object in the first image further comprising:

calculating distances from the plurality of initial feature points to center points of the grid cells the plurality of initial feature points are located; and

determining, for each grid cell that includes at least one initial feature point, an initial feature point that is closest to the center of the grid cell as the feature point.

12. The non-transitory, computer-readable medium of claim 11 , further comprising:

determining a plurality of pre-matching feature points in the second image that correspond to the plurality of feature points based on direction information of the object in the first image and the object in the second image; and

matching the second image with a second grid; and wherein determining a plurality of matching feature points of the object in the second image further comprises determining center points of grid cells of the second grid that include at least one pre-matching feature point as the plurality of matching feature points in the second image.

13. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing instructions that, when executed by the one or more computers, perform operations comprising:

obtaining a first image of an object oriented in a first direction and a second image of the object oriented in a second direction;

determining a plurality of initial feature points of the object in the first image;

matching the first image with a first grid that includes a plurality of grid cells;

determining a plurality of feature points of the object in the first image, comprising:

determining a center point of a grid cell as a feature point of the grid cell if the grid cell does not include an initial feature point; and

filtering the plurality of initial feature points until the number of feature points is the same in each grid cell;

determining a plurality of matching feature points of the object in the second image that correspond to the plurality of feature points of the object in the first image;

calculating similarity values between the plurality of feature points and the corresponding plurality of matching feature points;

calculating depth values of the plurality of feature points;

calculating weighted depth values based on the similarity values and depth values; and

performing 3D modeling of the object based on the weighted depth values.

14. The computer-implemented system of claim 13 , wherein determining a plurality of feature points of the object in the first image further comprising:

calculating distances from the plurality of initial feature points to center points of the grid cells the plurality of initial feature points are located; and

determining, for each grid cell that includes at least one initial feature point, an initial feature point that is closest to the center of the grid cell as the feature point.

15. The computer-implemented system of claim 14 , further comprising:

determining a plurality of pre-matching feature points in the second image that correspond to the plurality of feature points based on direction information of the object in the first image and the object in the second image; and

matching the second image with a second grid; and wherein determining a plurality of matching feature points of the object in the second image further comprises determining center points of grid cells of the second grid that include at least one pre-matching feature point as the plurality of matching feature points in the second image.

16. The computer-implemented system of claim 15 , wherein determining the similarity values between the plurality of feature points and the corresponding plurality of matching feature points further comprises:

determining color values of all pixels within the grid cells of the first grid that include at least one feature point;

determining color values of all pixels within the grid cells of the second grid that include at least one matching feature point;

calculating average color value differences between all the pixels within the grid cells of the first grid that include at least one feature point and all the pixels within the grid cells of the second grid that include at least one matching feature point; and

determining the similarity values of the plurality of feature points and the corresponding plurality of matching feature points based on the average color value differences.