Method For 3D Object Reconstruction Research Articles

3D-RVP: A method for 3D object reconstruction from a single depth view using voxel and point

Three-dimensional object reconstruction technology has a wide range of applications such as augment reality, virtual reality, industrial manufacturing and intelligent robotics. Although deep learning-based 3D object reconstruction technology has developed rapidly in recent years, there remain important problems to be solved. One of them is that the resolution of reconstructed 3D models is hard to improve because of the limitation of memory and computational efficiency when deployed on resource-limited devices. In this paper, we propose 3D-RVP to reconstruct a complete and accurate 3D geometry from a single depth view, where R, V and P represent Reconstruction, Voxel and Point, respectively. It is a novel two-stage method that combines a 3D encoder-decoder network with a point prediction network. In the first stage, we propose a 3D encoder-decoder network with residual learning to output coarse prediction results. In the second stage, we propose an iterative subdivision algorithm to predict the labels of adaptively selected points. The proposed method can output high-resolution 3D models by increasing a small number of parameters. Experiments are conducted on widely used benchmarks of a ShapeNet dataset in which four categories of models are selected to test the performance of neural networks. Experimental results show that our proposed method outperforms the state-of-the-arts, and achieves about 2.7% improvement in terms of the intersection-over-union metric.

An accurate method for 3D object reconstruction from unordered sparse views

3D object reconstruction from depth scans has been long investigated problem, but complete solutions are still missing. Recent advances of consumer depth sensors resulted in a number of excellent methods for 3D object reconstruction, with the most impressive results obtained by KinectFusion and Visual Odometry. Both methods reconstruct an object from a dense set of RGB-D video frames, with the important restrictions that the object has to be static and camera transitions has to be smooth during the reconstruction. As an alternative, we propose a method which is more accurate, while being able to reconstruct the object from a sparse set of unordered depth scans. Our method starts by pairwise view matching which relies on robust point pair features. It is followed by the efficient graph-based view consistency check to produce an initial 3D model reconstruction. To improve the accuracy, we perform a simultaneous optimization of the camera positions and the object model, using depth and color information. A quantitative evaluation demonstrates that we can reconstruct objects with equal or better accuracy than the KinectFusion and Visual Odometry, but using significantly lower number of unordered input scans.

Parallelization of a method for dense 3D object reconstruction in structured light scanning

Optical three-dimensional shape measurement based on structured light has been widely used for 3D measurement in many different applications. Although many different methods for 3D object reconstruction have been proposed last years, surprisingly none of the proposals includes a parallelization study of the tasks executed on computers, preventing these methods from reaching their maximum performance. In this paper, we propose first the computational evaluation of a previously proposed 3D object reconstruction method. Based on that evaluation, we also propose the parallelization of the reconstruction method using the OpenMP API specification for shared-memory parallel programming. The results show that most of the execution time is consumed by the tasks depending on the I/O hardware (camera, projector, hard disk, etc.), in such a way that the tasks performed by the computer should be overlapped as much as possible with the tasks performed by the I/O hardware, for those scenarios where the acquisition of the images should be performed on-line. For those scenarios where all the images are already available, then the inherent parallelism of the application increases, allowing a reduction of the execution time that can reach up to a 82 %. These results validate the proposed parallelization as a valuable implementation for data centers that provide web services for 3D object reconstruction purposes.

Method for 3D Object Reconstruction Using Several Portion of 2D Images from the Different Aspects Acquired with Image Scopes Included in the Fiber Retractor

Method for 3D object reconstruction using several portions of 2D images from the different aspects which are acquired with image scopes included in the fiber retractor is proposed. Experimental results show a great possibilityfor reconstruction of acceptable quality of 3D object on the computer with several imageswhich are viewed from the different aspects of 2D images.

Three-dimensional photon counting integral imaging using Bayesian estimation

We propose a new estimation method for 3D object reconstruction using photon-counting integral imaging. Earlier studies used maximum likelihood estimation (MLE) as a classical statistical method to reconstruct 3D images from photon-counting elemental images. We use an alternative statistical method known as the Bayesian method, which is more flexible and may perform better than MLE in terms of the mean square error (MSE) metric. The performance of the new reconstruction method is illustrated and compared with MLE by using the MSE. To the best of our knowledge, this is the first report to use the Bayesian method for 3D reconstruction of photon-counting integral imaging.