Intrusive Indoor Plant Modeling Guided by Leaf Recognition

Due to the heavy self-occlusion of indoor plants,to capture the complete 3D information of a plant,users usually need to manually cut the leaves,scan them,and register the leaf scans together.In response to this problem,we propose a method to identify and select the leaves to crop using the instance detection network.To avoid manually labeling photos of real plant leaves for training,we summarize the leaf shape and distribution to build 3D plant models,then render lots of images with leaf contour information.An automatic intrusive reconstruction system for capturing the full 3D of indoor plants is introduced,which automatically and successively selects a leaf to cut from a captured image to obtain its 3D points and adjusts the camera view when nothing is detected.Three types of virtual indoor plants have been tested and evaluated.Results show that the reconstructed plant models capture the plants well.