Objective

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In recent years

with the continuous improvement of computer hardware performance and the continuous in depth research of virtual plant modeling and landscape design in the fields of film and television and games

plant shape modeling has become possible and necessary. As one of the important organs of plants

leaves have complex physiological and morphological characteristics

which are difficult to represent in virtual scenes. Therefore

3D reconstruction of plant leaves is a very challenging subject. Current 3D reconstruction methods have two main types. 1) External equipment such as laser or radar are used to measure the distance of the target by transmitting signals and then receiving the signals reflected by the target object to obtain the 3D shape of the surface. 2) Using the principle of binocular vision

two or more images are obtained from different perspectives in the same scene

then the 3D shape of the object is obtained according to the parallax between the images. The former requires the introduction of external equipment

which is costly and difficult to operate

whereas the latter needs to detect and match the feature points between the acquired image sequences due to the reconstruction based on multiple images. The acquisition of a single image is simple

and the problem of feature matching of multiple images is eliminated. However

due to the less information contained in a single image and to recover the 3D shape of plant leaves from the limited information

this paper is based on the method of the shape from shading (SFS) and preprocesses the image to add a priori to the 3D shape estimation. The brightness statistic information obtained from the image and the prior information of plant morphological characteristics are used to recover the final 3D shape of the leaf.

Method

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When restoring the 3D shape of a single plant leaf image

the restoration of the surface shape is divided into two aspects: surface detail and surface macroscopic geometric shape. First

based on SFS

a distance field offset algorithm is designed according to the image skeleton to enhance the surface details of 3D shapes. The edge detection method is used to detect the leaf veins as the skeleton of the image

and the distance from the image skeleton is used as the constraint of the SFS minimization method to enhance the surface detail display. Then

to address the deficiencies of SFS in recovering the macroscopic geometric shape

various factors that affect the macroscopic geometric shape are considered

and the characteristics of leaf surface unevenness and curvature are finally realized. Selecting control points according to the statistical distribution of image brightness is proposed to control the change of the surface macroscopic geometric shape

and the distance field constraint of the blade central axis is used to restore the macroscopic geometric shape. According to the brightness statistics

the image is divided into bright-dark areas

the centroid of the unconnected areas of the bright and dark regions is used as the control point

and the cubic Bezier surface is used to generate the concave and convex characteristics of the blade surface. The overall bending of the blade can be estimated based on the distance from each point of the blade to the central axis because most blades have a certain degree of bending about the central axis and the curvature at the central axis changes the most. The two weights for the restoration of the surface macroscopic geometric shape are set based on the similarity between the restored reflection map and the input image

and the surface details are finally added to the macroscopic geometric shape to obtain the final 3D shape of the target object.

Result

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Plant leaf images are selected for experiments

and their 3D restoration results are compared with those of other methods (including Tsai linear approach

Zheng minimization approach

SIRFS(shape

illumination

and reflectance from shading)

and variational approach). Experimental results show that the method proposed enhances the display of surface details and has evident changes in macroscopic geometric shape. To verify the applicability of the method in the restoration of object surface details

namely

recovering the surface details of coins and dinosaurs

the experimental results prove that the proposed method of enhancing surface details is also applicable to other objects. In addition

using the ratio of error to information entropy is proposed to describe the effect of 3D reconstruction of the target. Error describes the accuracy of restoration

and information entropy describes the richness of information. The larger the entropy is

the greater the difference between the depth of restoration

which means larger surface macroscopic geometry changes. When the error is smaller and the information entropy is larger

the ratio of error to information entropy is smaller and the recovery effect is better.

Conclusion

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To address the 3D reconstruction problem of a single plant leaf image

decomposing the problem into two aspects is proposed: surface details and macroscopic geometric shape. Based on SFS

the surface details are enhanced according to the skeleton feature

the surface macroscopic geometric shape is jointly restored using the statistical distribution of image brightness and the axial distance field constraints of the leaves

the final 3D shape is obtained by combining the surface details and the macroscopic geometric shape

and the feasibility of the proposed method is verified by multiple sets of experiments.