scholarly journals Active Object Reconstruction Using a Guided View Planner

2018 ◽  
Author(s):  
Xin Yang ◽  
Yuanbo Wang ◽  
Yaru Wang ◽  
Baocai Yin ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Information Acquisition ◽  
3D Model ◽  
Alternative Methods ◽  
Active Object ◽  
Reconstruction Accuracy ◽  
Object Reconstruction ◽  
View Planning ◽  
Aggregate Information ◽  
Active Reconstruction ◽  
Reconstruction Model

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.

scholarly journals An active reconstruction algorithm based on partial prior information

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090420
Author(s):  
Yanzi Kong ◽  
Feng Zhu ◽  
Yingming Hao ◽  
Haibo Sun ◽  
Yilin Xie ◽  
...  
Keyword(s):  
Prior Information ◽  
Active Vision ◽  
Reconstruction Algorithm ◽  
Shape Estimation ◽  
Object Reconstruction ◽  
View Planning ◽  
Visual Tasks ◽  
Motion Paths ◽  
Active Reconstruction ◽  
Next Best View

Active reconstruction is an intelligent perception method that achieves object modeling with few views and short motion paths by systematically adjusting the parameters of the camera while ensuring model integrity. Part of the object information is always known for vision tasks in real scenes, and it provides some guidance for the view planning. A two-step active reconstruction algorithm based on partial prior information is presented, which includes rough shape estimation phase and complete object reconstruction phase, and both of them introduce the concept of active vision. An information expression method is proposed that can be used to manually initialize the repository according to specific visual tasks, and then the prior information and detected information are used to plan the next best view online until the object reconstruction is completed. The method is evaluated with simulated experiments and the result is compared with other methods.

A Discussion of Building a 3D Model Database and Information Query Plat-Form of Huizhou Traditional Dwellings Decorative Art

2012 ◽  
Vol 201-202 ◽  
pp. 991-995
Author(s):  
Xing She ◽  
Hai Bo Wang ◽  
Wang Qun Xiao ◽  
Qun Yan
Keyword(s):  
Information Acquisition ◽  
3D Model ◽  
Design Method ◽  
Digital Information ◽  
Design Practice ◽  
Decorative Art ◽  
Digital Contents ◽  
Terminal Design ◽  
Model Database ◽  
Plat Form

This thesis has demonstrated the feasibility of setting up the 3D model database and information query platform of Huizhou traditional dwellings decorative art, by using the digital information acquisition and processing technology, and summarized the terminal design method as well. It provides a technical service means and ways for the research of Hui-style dwellings construction, inheritance of Hui-culture and the related art design practice. This terminal provides a new model of the promotion, propaganda, application and development to the world, for the Hui-style dwellings construction decorative art. It will enrich the digital contents of Hui culture, which has the important practical and strategic significance to the construction of "digital Anhui".

Volumetric view planning for 3D reconstruction with multiple manipulators

2015 ◽  
Vol 42 (6) ◽  
pp. 533-543 ◽  
Author(s):  
Liangzhi Li ◽  
Nanfeng Xiao
Keyword(s):  
Three Dimensional ◽  
Industrial Applications ◽  
Computationally Efficient ◽  
Object Reconstruction ◽  
View Planning ◽  
Reconstruction Process ◽  
Content Type ◽  
Speed Up ◽  
Next Best View ◽  
Multiple Manipulators

Purpose – This paper aims to propose a new view planning method which can be used to calculate the next-best-view (NBV) for multiple manipulators simultaneously and build an automated three-dimensional (3D) object reconstruction system, which is based on the proposed method and can adapt to various industrial applications. Design/methodology/approach – The entire 3D space is encoded with octree, which marks the voxels with different tags. A set of candidate viewpoints is generated, filtered and evaluated. The viewpoint with the highest score is selected as the NBV. Findings – The proposed method is able to make the multiple manipulators, equipped with “eye-in-hand” RGB-D sensors, work together to accelerate the object reconstruction process. Originality/value – Compared to the existed approaches, the proposed method in this paper is fast, computationally efficient, has low memory cost and can be used in actual industrial productions where the multiple different manipulators exist. And, more notably, a new algorithm is designed to speed up the generation and filtration of the candidate viewpoints, which can guarantee both speed and quality.

scholarly journals Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Dong Zhang ◽  
Yongshun Zhang ◽  
Cunqian Feng
Keyword(s):  
Computational Efficiency ◽  
Taylor Expansion ◽  
Sparse Matrix ◽  
Doa Estimation ◽  
Reconstruction Problem ◽  
Reconstruction Accuracy ◽  
Mismatch Problem ◽  
Matrix Reconstruction ◽  
Simulation Results ◽  
Reconstruction Model

Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

scholarly journals Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Fan ◽  
Xiaoli Zhou ◽  
Shuo Chen ◽  
Zhijie Jiang ◽  
Yongqiang Cheng
Keyword(s):  
Model Errors ◽  
Target Image ◽  
Reconstruction Accuracy ◽  
Unified Framework ◽  
Imaging Model ◽  
Error Calibration ◽  
Coincidence Imaging ◽  
Bayesian Prior ◽  
Accurate Imaging ◽  
Reconstruction Model

Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential. Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably. Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse Bayesian approach. Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors. The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration. Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.

A smartphone photogrammetry method for digitizing prosthetic socket interiors

2016 ◽  
Vol 41 (2) ◽  
pp. 210-214 ◽  
Author(s):  
Amaia Hernandez ◽  
Edward Lemaire
Keyword(s):  
3D Model ◽  
Low Cost ◽  
3D Models ◽  
Reconstruction Accuracy ◽  
Linear Measurements ◽  
Prosthetic Socket ◽  
Cad Cam ◽  
3D Scanners ◽  
The Cost ◽  
Cam Systems

Background and Aim: Prosthetic CAD/CAM systems require accurate 3D limb models; however, difficulties arise when working from the person’s socket since current 3D scanners have difficulties scanning socket interiors. While dedicated scanners exist, they are expensive and the cost may be prohibitive for a limited number of scans per year. A low-cost and accessible photogrammetry method for socket interior digitization is proposed, using a smartphone camera and cloud-based photogrammetry services. Technique: 15 two-dimensional images of the socket’s interior are captured using a smartphone camera. A 3D model is generated using cloud-based software. Linear measurements were comparing between sockets and the related 3D models. Discussion: 3D reconstruction accuracy averaged 2.6 ± 2.0 mm and 0.086 ± 0.078 L, which was less accurate than models obtained by high quality 3D scanners. However, this method would provide a viable 3D digital socket reproduction that is accessible and low-cost, after processing in prosthetic CAD software. Clinical relevance The described method provides a low-cost and accessible means to digitize a socket interior for use in prosthetic CAD/CAM systems, employing a smartphone camera and cloud-based photogrammetry software.

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