Fast and Robust 3D Reconstruction Solution from Permissive Open-Source Code

With the growth of access to faster computers and more powerful cameras, the 3D reconstruction of objects has become one of the public's main topics of research and demand. This task is vigorously applied in creating virtual environments, creating object models, and other activities. One of the techniques for obtaining 3D features is photogrammetry, mapping objects and scenarios using only images. However, this process is very costly and can be pretty time-consuming for large datasets. This paper proposes a robust, efficient reconstruction pipeline with a low runtime in batch processing and permissive code. It is even possible to commercialize it without the need to keep the code open. We mix an improved structure from motion algorithm and a recurrent multi-view stereo reconstruction. We also use the Point Cloud Library for normal estimation, surface reconstruction, and texture mapping. We compare our results with state-of-the-art techniques using benchmarks and our datasets. The results showed a decrease of 69.4% in the average execution time, with high quality but a greater need for more images to achieve complete reconstruction.

An update on the Solar System Treks Mosaic Pipeline: public server and new mosaic products

<p>Solar System Treks (https://trek.nasa.gov) allows you to exploring the solar system in your web browser. Many of the digital terrain models and orthoimage mosaics viewable on Solar System Treks were created using a custom kubernetes-based pipeline, which we released open source in 2020 as the Solar System Treks Mosaic Pipeline (SSTMP). SSTMP manages workflows based on open-source applications including USGS ISIS and Ames Stereo Pipeline. It contains several templates for mosaic workflows to create stereo and imagery mosaics from Lunar Reconaissance Orbiter Narrow Angle Camera imagery, and is eventually will be expanded to include workflow templates to process imagery from a wide range of orbiters on all of the bodies that Solar System Treks supports (e.g. Mars, Venus, Ceres, Vesta, Bennu, Europa, etc.)</p> <p>Until recently, using SSTMP required installation on a Kubernetes cluster, which is a significant barrier to entry for many potential users. In response to this, we are creating a public-facing SSTMP server which allows turnkey mosaic creation. The user only needs to go to the website and select the area of interest, and SSTMP does the rest: determining the best imagery, downloading and ingesting it, computing stereo reconstruction, merging and formatting the result. Here we present the status of this tool and a demonstration.</p> <p>Additionally, we present new imagery products recently produced by SSTMP, including some experimental stereo reconstructions in the south polar region of the moon.</p>

Simulation of Images and Digital Terrain Models for the Mission BepiColombo

BepiColombo mission will provide Digital Terrain Models of the surface of Mercury by means of the stereo channel of the SIMBIO-SYS (Spectrometer and Imaging for MPO BepiColombo Integrated Observatory SYStem) imaging package onboard. The work here described presents a novel approach for the creation of higher resolution stereo products using the high-resolution channel of SIMBIO-SYS. Being the camera rigidly integrated with the spacecraft, this latter must be tilted to acquire stereo pairs necessary for the 3D reconstruction. A new method for image simulation and stereo reconstruction is presented in this work, where the input data are chosen as closely as possible to the real mission parameters. Different simulations are executed changing the illumination conditions and the stereo angles. The Digital Terrain Models obtained are evaluated and an analysis of the best acquisition conditions is performed, helping to improve the image acquisition strategy of BepiColombo mission. In addition, a strategy for the creation of a mosaic from different images acquired with the high-resolution channel of SIMBIO-SYS is explained, giving the possibility to obtain tridimensional products of extended targets.

Three-Dimensional Reconstruction of Indoor and Outdoor Environments Using a Stereo Catadioptric System

In this work, we present a panoramic 3D stereo reconstruction system composed of two catadioptric cameras. Each one consists of a CCD camera and a parabolic convex mirror that allows the acquisition of catadioptric images. We describe the calibration approach and propose the improvement of existing deep feature matching methods with epipolar constraints. We show that the improved matching algorithm covers more of the scene than classic feature detectors, yielding broader and denser reconstructions for outdoor environments. Our system can also generate accurate measurements in the wild without large amounts of data used in deep learning-based systems. We demonstrate the system’s feasibility and effectiveness as a practical stereo sensor with real experiments in indoor and outdoor environments.

Hilbert stereo reconstruction algorithm based on depth feature and stereo matching

This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert’s disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance.