The research field of reconstructing 3D models from 2D images is becoming more and more important. Existing methods typically perform single-view reconstruction or multi-view reconstruction utilizing the properties of recurrent neural networks. Due to the self-occlusion of the model and the special nature of the recurrent neural network, these methods have some problems. We propose a novel three-dimensional fusion hierarchical reconstruction method that utilizes a multi-view feature combination method and a hierarchical prediction strategy to unify the single view and any number of multiple views 3D reconstructions. Experiments show that our method can effectively combine features between different views and obtain better reconstruction results than the baseline, especially in the thin parts of the object. Our source code is available at https://github.com/VIM-Lab/3D-FHNet.

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3D-FHNet: Three-Dimensional Fusion Hierarchical Reconstruction Method for Any Number of Views

One problem in 3D reconstruction from aerial photographs is the evaluation of the point clouds quality. For point clouds, in general, different authors evaluate their results in different ways. This paper analyzes the existing evaluation methods for the point cloud quality and a new discussion regarding their applicability to aerial photographs is opened. Some of these methods are chosen based on practical issues and applied to a pair of reconstructions. The principal conclusion is that objective methods are the most interesting in photogrammetry applications, particularly the comparison between two point clouds.

A Review of Existing Evaluation Methods for Point Clouds Quality

This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.

Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo.

Reconstruction of 3D objects and their application in practice is more and more studying. The input data are acquired from different devices and techniques. They are processed based on geometric modeling, image processing techniques, and computer graphics. The output data are 3D models that represent their initial shapes of the real-world objects. This paper presents research for reconstructing the 3D objects by using an RGB-D camera. The main contribution of the paper includes studying and analyzing several methods to reconstruct a 3D object from a set of 3D point clouds acquired from different devices. Our proposed method is then performed based on a combination of geometric modeling and images processing to build a 3D model of an object from a set of noisy 3D point clouds. We have also implemented and compared obtained results between the methods to determine the most suitable in term of precision and robustness on noisy real data captured by the Microsoft Kinect.

A Research for Reconstructing 3D Object by Using an RGB-D Camera

The recent focus on virtual environments and 3D object scanning has highlighted the need for accurate and efficient methods to stitch concurrent point clouds into solid three-dimensional (3D) models. To address this need, we introduce a novel iterative approach for 3D multi-angle point cloud stitching using an iterative closest point (ICP) algorithm augmented with k-nearest neighbors (kNN). With this combined algorithm, our method focuses on minimizing the error between neighboring point clouds, allowing us to easily compute the necessary transformation to combine point clouds into one model. Thus, when given concurrent point clouds captured at multiple angles of the same object, our approach provides a single accurate 3D model. We evaluated the ability of the proposed framework to stitch multiple point clouds into a solid model by stitching a segmented model and comparing the root mean squared error to a standard iterative closest-point stitching algorithm. The experiments results shows that our method provides benefits in terms of efficiency and accuracy compared to a standard approach.

3D Multi-Angle Point Cloud Stitching Using Iterative Closest-point Stitching and K-Nearest-Neighbors

The acquisition of 3D models is a fundamental research in the field of computer graphics and computer vision. However, it is very complicated and expensive to build 3D models manually by using modeling software, such as 3DMAX and Maya. Therefore, how to get 3D models directly and quickly from the real world is a hot research topic in this field. Multi-view based 3D reconstruction techniques include feature point extraction and matching, camera calibration, sparse point cloud reconstruction, dense point cloud reconstruction, Poisson surface reconstruction, texture mapping and many other techniques. This paper will focus on the main algorithms of 3D reconstruction algorithms - Structure from Motion, Multi-View Stereo and Poisson Surface Reconstruction. Based on the above research, this paper designed a 3D reconstruction system including the above algorithms. The system takes a variety of views of an object as input, and finally outputs the 3D model of the object. Finally, this paper verified the effectiveness and feasibility of the system through experimental analysis.

Xu Chen

Papers

Research on 3D Reconstruction Based on Multiple Views