Three-dimensional segmentation of digital models utilizing soft classification geometric tuning

1. A computer-implemented method of selecting and manipulating segments of three-dimensional digital models, comprising:

determining a soft classification of a three-dimensional digital model, from a plurality of soft classifications, by analyzing features of the three-dimensional digital model based on a plurality of training digital models and training soft classification categories;

identifying a plurality of segmentation algorithms for segmenting digital models;

utilizing the soft classification of the three-dimensional digital model determined from the plurality of soft classifications to select a segmentation algorithm to apply to the three-dimensional digital model from the plurality of segmentation algorithms;

receiving an indication of a selection of a portion of the three-dimensional digital model; and

identifying a segment of the three-dimensional digital model corresponding to the selection utilizing the segmentation algorithm selected utilizing the soft classification corresponding to the three-dimensional digital model.

2. The method of claim 1 , wherein determining the soft classification of the three-dimensional digital model comprises utilizing a soft classification algorithm; and

further comprising training the soft classification algorithm prior to receiving the indication of the selection by:

providing the training digital models to the soft classification algorithm;

for each training digital model, utilizing the soft classification algorithm to predict at least one soft classification category corresponding to the training digital model; and

for each training digital model, comparing the at least one predicted soft classification category with a training soft classification category corresponding to the training digital model.

3. The method of claim 1 , wherein determining the soft classification comprises determining, for each soft classification category of a plurality of soft classification categories, a probability that that the three-dimensional digital model corresponds to the soft classification category.

4. The method of claim 3 , wherein determining the segmentation algorithm comprises determining a correspondence between the soft classification category of the plurality of soft classification categories and the segmentation algorithm from the plurality of segmentation algorithms.

5. The method of claim 4 , wherein determining the segmentation algorithm comprises comparing a first probability, from the soft classification, that the three-dimensional digital model corresponds to the soft classification category with a second probability, from the soft classification, that the three-dimensional digital model corresponds to a second soft classification category.

6. The method of claim 3 , wherein determining the segmentation algorithm from the plurality of segmentation algorithms further comprises:

selecting a first segmentation algorithm from the plurality of segmentation algorithms based on a first probability from the soft classification; and

selecting a second segmentation algorithm different from the first segmentation algorithm from the plurality of segmentation algorithms based on a second probability from the soft classification.

7. The method of claim 6 , further comprising:

generating a first segmentation parameter for the three-dimensional digital model utilizing the segmentation algorithm;

generating a second segmentation parameter for the three-dimensional digital model utilizing the second segmentation algorithm; and

generating a mixed segmentation parameter based on the first probability, the second probability, the first segmentation parameter, and the second segmentation parameter.

8. The method of claim 7 , wherein identifying the segment of the three-dimensional digital model corresponding to the selection utilizing the segmentation algorithm comprises identifying the segment of the three-dimensional digital model utilizing the mixed segmentation parameter.

9. The method of claim 3 , wherein determining the segmentation algorithm comprises:

comparing a probability threshold with a first probability, from the soft classification, that the three-dimensional digital model corresponds to a first classification category; and

based on a determination that the first probability exceeds the probability threshold, selecting the segmentation algorithm.

10. The method of claim 1 , wherein:

determining the segmentation algorithm comprises determining an input parameter based on the soft classification; and

utilizing the segmentation algorithm comprises utilizing the input parameter determined based on the soft classification to identify the segment of the three-dimensional digital model.

11. A system for selecting segments of three-dimensional digital models, comprising:

one or more memories storing a set of instructions comprising:

a soft classification algorithm trained to generate soft classifications of three-dimensional digital models from a plurality of soft classifications, the soft classifications comprising probabilities that a given three-dimensional digital model corresponds to one or more soft classification categories in a set of soft classification categories;

a plurality of segmentation algorithms, wherein each segmentation algorithm corresponds to a soft classification category from the set of soft classification categories; and

a three-dimensional digital model comprising a plurality of vertices; and

at least one computing device storing instructions thereon, that, when executed by the at least one computing device, cause the system to:

determine a soft classification of the three-dimensional digital model, from the plurality of soft classifications, utilizing the soft classification algorithm;

utilize the soft classification of the three-dimensional digital model determined from the plurality of soft classifications to select a segmentation algorithm to apply to the three-dimensional digital model from the plurality of segmentation algorithms;

receive an indication of a selection of a portion of the three-dimensional digital model; and

identify a segment of the three-dimensional digital model corresponding to the selected portion of the three-dimensional digital model utilizing the segmentation algorithm selected based on the soft classification of the three-dimensional digital model.

12. The system of claim 11 , further comprising instructions that, when executed by the at least one computing device, cause the system to: determine the soft classification by determining, for each soft classification category in the set of soft classification categories, a probability that that the three-dimensional digital model corresponds to the soft classification category.

13. The system of claim 12 , further comprising instructions that, when executed by the at least one computing device, cause the system to:

select the segmentation algorithm from the plurality of segmentation algorithms based on a first probability from the soft classification;

select a second segmentation algorithm from the plurality of segmentation algorithms based on a second probability from the soft classification;

generate a first segmentation parameter for the three-dimensional digital model utilizing the segmentation algorithm;

generate a second segmentation parameter for the three-dimensional digital model utilizing the second segmentation algorithm;

generate a mixed segmentation parameter based on the first segmentation parameter, the second segmentation parameter, and mixture coefficients; and

apply the segmentation algorithm to the three-dimensional digital model by identifying the segment of the three-dimensional digital model utilizing the mixed segmentation parameter.

14. The system of claim 13 , wherein:

the first segmentation parameter comprises a first edge segmentation score corresponding to an edge of the three-dimensional digital model, wherein the first edge segmentation score reflects a cost of cutting the three-dimensional digital model at the edge;

the second segmentation parameter comprises a second edge segmentation score corresponding to the edge of the three-dimensional digital model, wherein the second edge segmentation score reflects a second cost of cutting the three-dimensional digital model at the edge; and

the mixed segmentation parameter comprises a mixed edge segmentation score reflecting a combination of the first edge segmentation score and the second edge segmentation score based on the first probability and the second probability.

15. The system of claim 12 , further comprising instructions that, when executed by the at least one computing device, cause the system to:

compare a probability threshold and a first probability, from the soft classification, that the three-dimensional digital model corresponds to a first soft classification category; and

in response to a determination that the first probability exceeds the probability threshold, select the segmentation algorithm and exclude the remainder of the segmentation algorithms in identifying the segment of the three-dimensional digital model.

16. The system of claim 11 , further comprising instructions that, when executed by the at least one computing device, cause the system to:

determine an input parameter of the segmentation algorithm based on the soft classification; and

apply the segmentation algorithm utilizing the input parameter determined based on the soft classification to identify the segment of the three-dimensional digital model corresponding to the selection.

17. A non-transitory computer readable medium storing instructions thereon that, when executed by at least one processor, cause a computer system to:

determine a soft classification of a three-dimensional digital model, from a plurality of soft classifications, by analyzing features of the three-dimensional digital model utilizing a soft classification algorithm;

identify a plurality of segmentation algorithms for segmenting digital models;

utilize the soft classification of the three-dimensional digital model determined from the plurality of soft classifications to select a segmentation algorithm to apply to the three-dimensional digital model from the plurality of segmentation algorithms;

receive an indication of a selection of a portion of the three-dimensional digital model; and

identify a segment of the three-dimensional digital model corresponding to the selection utilizing the segmentation algorithm selected utilizing the soft classification corresponding to the three-dimensional digital model.

18. The non-transitory computer readable medium of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the computer system to determine the segmentation algorithm by determining a correspondence between a soft classification category of a plurality of soft classification categories and the segmentation algorithm from the plurality of segmentation algorithms.

19. The non-transitory computer readable medium of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the computer system to:

determine an additional soft classification of an additional three-dimensional digital model; and

utilize the additional soft classification of the additional three-dimensional digital model to select an additional segmentation algorithm from the plurality of segmentation algorithms.

20. The non-transitory computer readable medium of claim 19 , further comprising instructions that, when executed by the at least one processor, cause the computer system to identify an additional segment of the additional three-dimensional digital model utilizing the additional segmentation algorithm from the plurality of segmentation algorithms.