@article{lu_simul_2021,
  author = {Lu, Yawen and Wang, Yuxing and Parikh, Devarth and Khan, Awais and Lu, Guoyu},
  journal = {IEEE Transactions on Image Processing},
  title = {Simultaneous Direct Depth Estimation and Synthesis Stereo for Single Image Plant Root Reconstruction},
  year = {2021},
  volume = {30},
  pages = {4883-4893},
  doi = {10.1109/TIP.2021.3069578},
  freepdf = {https://drive.google.com/file/d/1flz3VJ_Nix18wPcz6btxxJDo2B7TwLzs/view},
  tldr = {Root reconstruction from one image of young apple tree roots. Two approaches to generating depth maps; first is to predict depth map from one image. The second is to generate another image from a single image, and then generate the depth map using a stereo technique. The results are combined to form the resulting point cloud.},
  keywords = {Three-dimensional displays;Image reconstruction;Estimation;Solid modeling;Shape;Cameras;Periodic structures;Root reconstruction;cross-view synthesis;single image depth estimation}
}

Plant roots are the main conduit to its interaction with the physical and biological environment. A 3D root system architecture can provide fundamental and applied knowledge of a plant’s ability to thrive, but the construction of 3D structures for thin and complicated plant roots is challenging. Existing methods such as structure-from-motion and shape-from-silhouette require multiple images, as input, under a complicated optimization process, which is usually not convenient in fieldwork. Little effort has been put into investigating the applications of deep neural network methods to reconstruct thin objects, like plant root systems, from a single image. We propose an unsupervised learning scheme to estimate the root depth from only one image as input, which is further applied to reconstruct the complete root system. The boundaries of the reconstructed object usually contain large errors, which is a significant problem for roots with many thin branches. To reduce reconstruction errors, we integrate a cross-view GAN-based network into the reconstruction process, which predicts the root image from a different perspective. Based on the predicted view, we reconstruct the root system using stereo reconstruction, which helps to identify the accurately reconstructed points by enforcing their consistency. The results on both the real plant root dataset and the synthetic dataset demonstrate the effectiveness of the proposed algorithm compared with state-of-the-art single image 3D reconstruction models on plant roots.

Publisher: http://doi.org/10.1109/TIP.2021.3069578
pdf: https://drive.google.com/file/d/1flz3VJ_Nix18wPcz6btxxJDo2B7TwLzs/view