3D reconstruction and positioning of surface features based on a monocular camera and geometric constraints

2021 ◽  
Vol 61 (6) ◽  
pp. C27
Author(s):  
Guanyu Hou ◽  
Weibin Zhang ◽  
Bin Wu ◽  
Rongfang He
Keyword(s):  
3D Reconstruction ◽  
Geometric Constraints ◽  
Surface Features ◽  
Monocular Camera

scholarly journals A 3D Reconstruction Framework of Buildings Using Single Off-Nadir Satellite Image

2021 ◽  
Vol 13 (21) ◽  
pp. 4434
Author(s):  
Chunhui Zhao ◽  
Chi Zhang ◽  
Yiming Yan ◽  
Nan Su
Keyword(s):  
Remote Sensing ◽  
3D Reconstruction ◽  
Spatial Resolution ◽  
Satellite Image ◽  
Geometric Constraints ◽  
Single Image ◽  
Multiple Images ◽  
Angle Size ◽  
Reconstruction Methods ◽  
Mesh Models

A novel framework for 3D reconstruction of buildings based on a single off-nadir satellite image is proposed in this paper. Compared with the traditional methods of reconstruction using multiple images in remote sensing, recovering 3D information that utilizes the single image can reduce the demands of reconstruction tasks from the perspective of input data. It solves the problem that multiple images suitable for traditional reconstruction methods cannot be acquired in some regions, where remote sensing resources are scarce. However, it is difficult to reconstruct a 3D model containing a complete shape and accurate scale from a single image. The geometric constraints are not sufficient as the view-angle, size of buildings, and spatial resolution of images are different among remote sensing images. To solve this problem, the reconstruction framework proposed consists of two convolutional neural networks: Scale-Occupancy-Network (Scale-ONet) and model scale optimization network (Optim-Net). Through reconstruction using the single off-nadir satellite image, Scale-Onet can generate water-tight mesh models with the exact shape and rough scale of buildings. Meanwhile, the Optim-Net can reduce the error of scale for these mesh models. Finally, the complete reconstructed scene is recovered by Model-Image matching. Profiting from well-designed networks, our framework has good robustness for different input images, with different view-angle, size of buildings, and spatial resolution. Experimental results show that an ideal reconstruction accuracy can be obtained both on the model shape and scale of buildings.

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