Real-time 3D Reconstruction System Research Articles

Automatic Real-Time Creation of Three-Dimensional (3D) Representations of Objects, Buildings, or Scenarios Using Drones and Artificial Intelligence Techniques

This work presents the development and evaluation of a real-time 3D reconstruction system using drones. The system leverages innovative artificial intelligence techniques in photogrammetry and computer vision (CDS-MVSNet and DROID-SLAM) to achieve the accurate and efficient reconstruction of 3D environments. By integrating vision, navigation, and 3D reconstruction subsystems, the proposed system addresses the limitations of existing applications and software in terms of speed and accuracy. The project encountered challenges related to scheduling, resource availability, and algorithmic complexity. The obtained results validate the applicability of the system in real-world scenarios and open avenues for further research in diverse areas. One of the tests consisted of a one-minute-and-three-second flight around a small figure, while the reconstruction was performed in real time. The reference Meshroom software completed the 3D reconstruction in 136 min and 12 s, while the proposed system finished the process in just 1 min and 13 s. This work contributes to the advancement in the field of 3D reconstruction using drones, benefiting from advancements in technology and machine learning algorithms.

Real-time 3D reconstruction system using multi-task feature extraction network and surfel

Real-time 3D reconstruction has always been a hot problem in mobile robotics. However, feature extraction algorithms used in traditional 3D reconstruction systems cannot work stably in challenging environments such as low-textured areas. The feature extraction method based on deep learning has higher accuracy and stability than traditional methods, but the complicated network structure leads to the lack of real-time performance. To overcome the limitations, a real-time 3D reconstruction system using multi-task feature extraction network and surfel is proposed. To enhance the stability and accuracy, we design a simplified convolutional neural network to extract feature. Moreover, surfel model is employed to implement the fusion and optimization of 3D point cloud. According to the experiments on the public dataset and real environments, the proposed system can run on Robot Operating System in real time, maintain high pose estimation accuracy in challenging scenes, and complete precise 3D reconstruction. The overall performance of the proposed system is better than that of the traditional 3D reconstruction system.

Optimized Method for Real-time Texture Reconstruction with RGB-D Camera

With the appearance of RGB-D camera, the field of three-dimensional (3D) reconstruction receives more and more attention. In this paper, we present an optimization approach to produce high-quality textured 3D models based on the real-time 3D reconstruction system. The resulting models of real-time texture reconstruction often suffer from blurring, ghosting, and other artifacts. Our approach addresses this texture quality problem using blur detection and an optimized weight function. Experimental results demonstrate that our approach can improve the quality of textured 3D models by reducing the blur and ghosts on the model surface.

A Real-Time 3D Reconstruction System for Screening Icosahedral Particles Under Different Conditions at the Microscope.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Parallel processing for real-time 3D reconstruction from video streams

Our ultimate aim was to achieve commodity telepresence systems capable of communicating both what someone looks like and what, within the technology joined space, they are looking at. Towards this we have implemented a previously distributed approach to reconstructing form from multiple video streams, so that it runs on a single computer. Importantly, the way in which the problem is parallelised has been optimised to reflect the various stages of the process rather than the need to minimise data communication across a network. The Exact Polyhedral Visual Hull (EPVH) algorithm had previously been distributed to achieve real time frame rates. EPVH has five sequential steps of which four were previously parallelised as two pairs. The metric for parallelisation of each pair was thus the best fit across both sequential steps within it and the outcome of the first stage of a pair could not determine the parallelisation of the second. We parallelised all five stages according to both distinct metrics and data from the previous stage. In this way we provided a better fit of parallelisation to both process and data. The study proposes a method of parallelisation theoretically more tailored to execution on a single machine, providing a detailed description of the implementation along with a number of optimisations to further improve performance and provides indicative results, for example multicore CPU and GPU platforms that might be of interest to researchers and practitioners wishing to implement a real-time 3D reconstruction system.