Attention-Based Dense Point Cloud Reconstruction From a Single Image

Lü, Qiang; Xiao, Mingjie; Lu, Yimin; Yuan, Xiaohui; Yu, Ye

doi:10.1109/access.2019.2943235

Cited by 30 publications

(18 citation statements)

References 28 publications

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“…The ill-posed problem of 3D reconstruction from a single image is addressed by their proposed two-pronged method. Other methods about 3D point clouds reconstruction from a single image can also be found in [27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

PDE-Based 3D Surface Reconstruction from Multi-View 2D Images

Zhu

Iglesias

et al. 2022

Mathematics

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Partial differential equation (PDE) based surfaces own a lot of advantages, compared to other types of 3D representation. For instance, fewer variables are required to represent the same 3D shape; the position, tangent, and even curvature continuity between PDE surface patches can be naturally maintained when certain conditions are satisfied, and the physics-based nature is also kept. Although some works applied implicit PDEs to 3D surface reconstruction from images, there is little work on exploiting the explicit solutions of PDE to this topic, which is more efficient and accurate. In this paper, we propose a new method to apply the explicit solutions of a fourth-order partial differential equation to surface reconstruction from multi-view images. The method includes two stages: point clouds data are extracted from multi-view images in the first stage, which is followed by PDE-based surface reconstruction from the obtained point clouds data. Our computational experiments show that the reconstructed PDE surfaces exhibit good quality and can recover the ground truth with high accuracy. A comparison between various solutions with different complexity to the fourth-order PDE is also made to demonstrate the power and flexibility of our proposed explicit PDE for surface reconstruction from images.

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Section: Related Workmentioning

confidence: 99%

PDE-Based 3D Surface Reconstruction from Multi-View 2D Images

Zhu

Iglesias

et al. 2022

Mathematics

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“…In [13], a generative modeling framework used 2D convolutional operation to predict multiple pre-defined depth images and use them to generate a dense 3D model. In [15], a two-stage training dense point cloud generation network was proposed. In the first stage, the network takes a single RGB image and generates a sparse point cloud.…”

Section: Related Workmentioning

confidence: 99%

Pixel2point: 3D Object Reconstruction From a Single Image Using CNN and Initial Sphere

et al. 2021

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“…Recent works combine Deep Learning techniques to perform three-dimensional reconstructions, using data from the stereo cameras, mono-LiDAR, and stereo-LiDAR cameras, merging merging the data from these sensors to obtain better results to obtain better results [35][36][37][38][39]. However, these proposals are focused on solutions for individual objects and many of them only focus on reconstructing structured environments.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Architecture for 3D Mapping Urban Landscapes

et al. 2021

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In this paper, an approach through a Deep Learning architecture for the three-dimensional reconstruction of outdoor environments in challenging terrain conditions is presented. The architecture proposed is configured as an Autoencoder. However, instead of the typical convolutional layers, some differences are proposed. The Encoder stage is set as a residual net with four residual blocks, which have been provided with the necessary knowledge to extract the feature maps from aerial images of outdoor environments. On the other hand, the Decoder stage is set as a Generative Adversarial Network (GAN) and called a GAN-Decoder. The proposed network architecture uses a sequence of the 2D aerial image as input. The Encoder stage works for the extraction of the vector of features that describe the input image, while the GAN-Decoder generates a point cloud based on the information obtained in the previous stage. By supplying a sequence of frames that a percentage of overlap between them, it is possible to determine the spatial location of each generated point. The experiments show that with this proposal it is possible to perform a 3D representation of an area flown over by a drone using the point cloud generated with a deep architecture that has a sequence of aerial 2D images as input. In comparison with other works, our proposed system is capable of performing three-dimensional reconstructions in challenging urban landscapes. Compared with the results obtained using commercial software, our proposal was able to generate reconstructions in less processing time, with less overlapping percentage between 2D images and is invariant to the type of flight path.

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Attention-Based Dense Point Cloud Reconstruction From a Single Image

Cited by 30 publications

References 28 publications

PDE-Based 3D Surface Reconstruction from Multi-View 2D Images

PDE-Based 3D Surface Reconstruction from Multi-View 2D Images

Pixel2point: 3D Object Reconstruction From a Single Image Using CNN and Initial Sphere

A Deep Learning Architecture for 3D Mapping Urban Landscapes

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