3D reconstruction of surface and subsurface structures of solids by SEM stereo images

2001 ◽  
pp. 137-140
Keyword(s):  
3D Reconstruction ◽  
Stereo Images ◽  
Subsurface Structures

scholarly journals DEEP LEARNING FOR 3D RECONSTRUCTION OF THE MARTIAN SURFACE USING MONOCULAR IMAGES: A FIRST GLANCE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1111-1116
Author(s):  
Z. Chen ◽  
B. Wu ◽  
W. C. Liu
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Experimental Results ◽  
Training Data ◽  
Network System ◽  
Stereo Images ◽  
Martian Surface ◽  
Preliminary Results ◽  
Low Level ◽  
Traditional Technology

Abstract. The paper presents our efforts on CNN-based 3D reconstruction of the Martian surface using monocular images. The Viking colorized global mosaic and Mar Express HRSC blended DEM are used as training data. An encoder-decoder network system is employed in the framework. The encoder section extracts features from the images, which includes convolution layers and reduction layers. The decoder section consists of deconvolution layers and is to integrate features and convert the images to desired DEMs. In addition, skip connection between encoder and decoder section is applied, which offers more low-level features for the decoder section to improve its performance. Monocular Context Camera (CTX) images are used to test and verify the performance of the proposed CNN-based approach. Experimental results show promising performances of the proposed approach. Features in images are well utilized, and topographical details in images are successfully recovered in the DEMs. In most cases, the geometric accuracies of the generated DEMs are comparable to those generated by the traditional technology of photogrammetry using stereo images. The preliminary results show that the proposed CNN-based approach has great potential for 3D reconstruction of the Martian surface.

scholarly journals From 2D Images to 3D Tangible Models: Autostereoscopic and Haptic Visualization of Martian Rocks in Virtual Environments

2007 ◽  
Vol 16 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Cagatay Basdogan
Keyword(s):  
3D Reconstruction ◽  
3D Models ◽  
Rock Surface ◽  
Surface Model ◽  
Stereo Images ◽  
Martian Surface ◽  
Reconstruction Process ◽  
Low Transmission ◽  
2D Images ◽  
Multimodal Visualization

A planetary rover acquires a large collection of images while exploring its surrounding environment. For example, 2D stereo images of the Martian surface captured by the lander and the Sojourner rover during the Mars Pathfinder mission in 1997 were transmitted to Earth for scientific analysis and navigation planning. Due to the limited memory and computational power of the Sojourner rover, most of the images were captured by the lander and then transmitted to Earth directly for processing. If these images were merged together at the rover site to reconstruct a 3D representation of the rover's environment using its on-board resources, more information could potentially be transmitted to Earth in a compact manner. However, construction of a 3D model from multiple views is a highly challenging task to accomplish even for the new generation rovers (Spirit and Opportunity) running on the Mars surface at the time this article was written. Moreover, low transmission rates and communication intervals between Earth and Mars make the transmission of any data more difficult. We propose a robust and computationally efficient method for progressive transmission of multi-resolution 3D models of Martian rocks and soil reconstructed from a series of stereo images. For visualization of these models on Earth, we have developed a new multimodal visualization setup that integrates vision and touch. Our scheme for 3D reconstruction of Martian rocks from 2D images for visualization on Earth involves four main steps: a) acquisition of scans: depth maps are generated from stereo images, b) integration of scans: the scans are correctly positioned and oriented with respect to each other and fused to construct a 3D volumetric representation of the rocks using an octree, c) transmission: the volumetric data is encoded and progressively transmitted to Earth, d) visualization: a surface model is reconstructed from the transmitted data on Earth and displayed to a user through a new autostereoscopic visualization table and a haptic device for providing touch feedback. To test the practical utility of our approach, we first captured a sequence of stereo images of a rock surface from various viewpoints in JPL MarsYard using a mobile cart and then performed a series of 3D reconstruction experiments. In this paper, we discuss the steps of our reconstruction process, our multimodal visualization system, and the tradeoffs that have to be made to transmit multiresolution 3D models to Earth in an efficient manner under the constraints of limited computational resources, low transmission rate, and communication interval between Earth and Mars.

scholarly journals Robust Detection of Point Correspondences in Stereo Images

10.14311/956 ◽  
2007 ◽  
Vol 47 (4-5) ◽  
Author(s):  
A. Stojanovic ◽  
M. Unger
Keyword(s):  
3D Reconstruction ◽  
Fundamental Matrix ◽  
Probabilistic Approach ◽  
Geometric Constraint ◽  
Stereo Images ◽  
Automatic Computation ◽  
Robust Detection ◽  
Image Pairs ◽  
New Criterion

A major challenge in 3D reconstruction is the computation of the fundamental matrix. Automatic computation from uncalibrated image pairs is performed from point correspondences. Due to imprecision and wrong correspondences, only an approximation of the true fundamental matrix can be computed. The quality of the fundamental matrix strongly depends on the location and number of point correspondences.Furthermore, the fundamental matrix is the only geometric constraint between two uncalibrated views, and hence it can be used for the detection of wrong point correspondences. This property is used by current algorithms like RANSAC, which computes the fundamental matrix from a restricted set of point correspondences. In most cases, not only wrong correspondences are disregarded, but also correct ones, which is due to the criterion used to eliminate outliers. In this context, a new criterion preserving a maximum of correct correspondences would be useful.In this paper we introduce a novel criterion for outlier elimination based on a probabilistic approach. The enhanced set of correspondences may be important for further computation towards a 3D reconstruction of the scene. 

scholarly journals Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images

2021 ◽  
Vol 13 (5) ◽  
pp. 839
Author(s):  
Zeyu Chen ◽  
Bo Wu ◽  
Wai Chung Liu
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Real Data ◽  
Experimental Results ◽  
Training Data ◽  
Stereo Images ◽  
Martian Surface ◽  
Laser Altimetry ◽  
3D Surface ◽  
Imaging Science

Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications.

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