Fig 2 - uploaded by Thomas Ebner
Content may be subject to copyright.
Source publication
The reconstruction of real world objects becomes even more important in the view of creating highly realistic scenes for Virtual Reality applications. In this paper, we present a fully automated algorithmic pipeline for high-quality 3D reconstruction of real world objects. The proposed method refines an initial 3D model by exploiting the results of...
Context in source publication
Context 1
... points (i.e. the denominator of the reconstruction ratio) is maximum. In subsequent steps, the stereo pair is chosen, which maximises the coverage ratio, i.e. the set of reconstructible points (defined as the union of the reconstructible points of all contained stereo pairs). Results of the automatic stereo pair selection are shown in Fig. 2. ...
Similar publications
With the rapid development of China, there is a huge demand for PE pipelines. At present, the detection methods in pipeline production are mainly manual sampling testing and ultrasonic testing, which cannot display the internal surface characteristics of the pipeline comprehensively and visually, so as to facilitate the subsequent non-destructive t...
The application of deep learning in the field of 3D reconstruction has greatly improved the quality of 3D object reconstruction. For methods that take the point cloud as supervision information, previous research has mainly focused on the network architecture while setting Chamfer Distance (CD) loss as the default loss function. However, CD only co...
This paper presents a novel multi-purpose benchmark for assessing the performance of the entire image-based pipeline for 3D urban reconstruction and 3D data classification. The proposed benchmark is called “3DOMcity” photogrammetric contest and is publicly available at https://3dom.fbk.eu/3domcity-benchmark. Compared to previous benchmark initiativ...
Three-dimensional Reconstruction has drawn much attention in computer vision. Generating a dense point cloud from a single image is a more challenging task. However, generating dense point clouds directly costs expensively in calculation and memory and may cause the network hard to train. In this work, we propose a two-stage training dense point cl...
3D Geometric quality inspection involves assessing and comparing a reconstructed object to a predefined reference model or design that defines its expected volume. Achieving precise 3D object geometry reconstruction from multiple views can be challenging. In this research, we propose a camera-coherent point selection method to measure differences w...
Citations
... The most widespread Multi-View Stereo methods select for each camera a pairing view among the others by relying on several factors. Li et al. [9] and Ebner et al. [10] evaluate the baseline and the angle of the principal viewing direction between the cameras; instead, other methods [11,12,13,14] consider the SfM 3D points and take into account the average angle between the camera-to-point viewing rays, the baseline among views and the scale. Vogiatzis et al. [15] leverage on similar metrics to filter out unreliable photometric measures, adopted to estimate the 3D model. ...
Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such representations, the estimated model does not capture fine details of the scene. Therefore a mesh refinement algorithm is usually applied; it improves the mesh resolution and accuracy by minimizing the photometric error induced by the 3D model into pairs of cameras. The choice of these pairs significantly affects the quality of the refinement and usually relies on sparse 3D points belonging to the surface. Instead, in this paper, to increase the quality of pairs selection, we exploit the 3D model (before the refinement) to compute five metrics: scene coverage, mutual image overlap, image resolution, camera parallax, and a new symmetry term. To improve the refinement robustness, we also propose an explicit method to manage occlusions, which may negatively affect the computation of the photometric error. The proposed method takes into account the depth of the model while computing the similarity measure and its gradient. We quantitatively and qualitatively validated our approach on publicly available datasets against state of the art reconstruction methods.
... Xu et al. [13] captured the inherent geometrical variation of underwater objects for reconstruction, requiring sufficient corresponding features among input images. Ebner et al. [14] proposed a fully automated algorithm for the 3D reconstruction of real objects, achieving a high-quality point cloud by exploiting pairwise stereo depth estimation. Shen [15] merged a depth map with a patch-based stereo matching process for reconstructing objects in large-scale scenes. ...
With the three-dimensional (3D) coordinates of objects captured by a sequence of images taken in different views, object reconstruction is a technique which aims to recover the shape and appearance information of objects. Although great progress in object reconstruction has been made over the past few years, object reconstruction in occlusion situations remains a challenging problem. In this paper, we propose a novel method to reconstruct occluded objects based on synthetic aperture imaging. Unlike most existing methods, which either assume that there is no occlusion in the scene or remove the occlusion from the reconstructed result, our method uses the characteristics of synthetic aperture imaging that can effectively reduce the influence of occlusion to reconstruct the scene with occlusion. The proposed method labels occlusion pixels according to variance and reconstructs the 3D point cloud based on synthetic aperture imaging. Accuracies of the point cloud are tested by calculating the spatial difference between occlusion and non-occlusion conditions. The experiment results show that the proposed method can handle the occluded situation well and demonstrates a promising performance.
Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such representations, the estimated model does not capture fine details of the scene. Therefore a mesh refinement algorithm is usually applied; it improves the mesh resolution and accuracy by minimizing the photometric error induced by the 3D model into pairs of cameras. The choice of these pairs significantly affects the quality of the refinement and usually relies on sparse 3D points belonging to the surface. Instead, in this paper, to increase the quality of pairs selection, we exploit the 3D model (before the refinement) to compute five metrics: scene coverage, mutual image overlap, image resolution, camera parallax, and a new symmetry term. To improve the refinement robustness, we also propose an explicit method to manage occlusions, which may negatively affect the computation of the photometric error. The proposed method takes into account the depth of the model while computing the similarity measure and its gradient. We quantitatively and qualitatively validated our approach on publicly available datasets against state of the art reconstruction methods.
To deal with the three-dimensional (3-D) point cloud loss caused by object reflection in the active fringe projection 3-D measurement, an active reflection suppression method for 3-D measurement is proposed. The method employs high-dynamic range images obtained by multiple exposure image fusion and a three-wavelength phase-shift profilometry method to achieve high-precision 3-D measurement of reflective objects. Experimental results show that, compared to traditional 3-D measurement methods, the proposed one can more effectively handle reflections thereby avoiding 3-D point cloud loss in the measurement of reflective objects.
