Indoor structures are composed of ceilings, walls and floors that need to be modeled for a variety of applications. This paper proposes an approach to reconstructing models of indoor structures in complex environments. First, semantic pre-processing, including segmentation and occlusion construction, is applied to segment the input point clouds to generate semantic patches of structural primitives with uniform density. Then, a primitives extraction method with detected boundary is introduced to approximate both the mathematical surface and the boundary of the patches. Finally, a constraint-based model reconstruction is applied to achieve the final topologically consistent structural model. Under this framework, both the geometric and structural constraints are considered in a holistic manner to assure topologic regularity. Experiments were carried out with both synthetic and real-world datasets. The accuracy of the proposed method achieved an overall reconstruction quality of approximately 4.60 cm of root mean square error (RMSE) and 94.10% Intersection over Union (IoU) of the input point cloud. The development can be applied for structural reconstruction of various complex indoor environments.

The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km²) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km² in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.