Multiple depth maps integration for 3D reconstruction using geodesic graph cuts

Depth images, in particular depth maps estimated from stereo vision, may have a substantial amount of outliers and result in inaccurate 3D modelling and reconstruction. To address this challenging issue, in this paper, a graph-cut based multiple depth maps integration approach is proposed to obtain smooth and watertight surfaces. First, confidence maps for the depth images are estimated to suppress noise, based on which reliable patches covering the object surface are determined. These patches are then exploited to estimate the path weight for 3D geodesic distance computation, where an adaptive regional term is introduced to deal with the “shorter-cuts” problem caused by the effect of the minimal surface bias. Finally, the adaptive regional term and the boundary term constructed using patches are combined in the graph-cut framework for more accurate and smoother 3D modelling. We demonstrate the superior performance of our algorithm on the well-known Middlebury multi-view database and additionally on real-world multiple depth images captured by Kinect. The experimental results have shown that our method is able to preserve the object protrusions and details while maintaining surface smoothness

SURVEYING DAMAGE TO HISTORIC BUILDINGS IN MEINONG EARTHQUAKE

[EN] In this paper, according to survey damage to historic building in Meinong earthquake, five damage models for Chinese temple, the state temple of the Martial God are proposed. These temples were built during 1600s. These five damage models are separation of column from wall (M1), roof damage (M3) wall cracks (M5), surface peeling (M6), and Tilt column (M8). Taiwan free field strong earthquake network is used to analyze seismic data from three free field stations. These stations are close to the state temple of the Martial God. Due to forward directivity effect, the stations’ maximum ground acceleration amplitude is dependent of distance between epicenter and station. Modal analysis using finite element method (FEM) is used to detect weak positions of Martial God temple. Weak positions of numerical results are close to site failure positions of Martial God temple.Wang, R.; Chang, H.; Lee, M. (2016). SURVEYING DAMAGE TO HISTORIC BUILDINGS IN MEINONG EARTHQUAKE. En 8th International congress on archaeology, computer graphics, cultural heritage and innovation. Editorial Universitat Politècnica de València. 488-491. https://doi.org/10.4995/arqueologica8.2015.4178OCS48849

Numerical simulation analysis of reinforced concrete syructure using vfife fiber-element

In this paper, a vector form intrinsic finite element (VFIFE or V-5 ) method is used to compute the nonlinear responses of reinforced concrete (RC) structure. In addition, the fiber-element model of VFIFE frame element is formulated. Material models of both concrete and steel on the cross section of the member are considered. The VFIFE method is a particle- based method. They have three key VFIFE processes such as the point value description, path element and convected material frame [1]. The RC structure is represented by finite particles. Each particle is subjected to the external forces and internal forces. The particle satisfies the Newton’s Law. A fictitious reversed rigid body motion is used to remove the rigid body motion from the deformations of the element [2]. The internal forces of the element in deformation coordinates satify the equilibrium equations. Comparing the results of numerical simulations and experiments of the reinforced concrete members subjected to external loads, the proposed method demonstrate accuracy and efficiency

Implementation and application of vector form intrinsic finite element in pushover analysis for reinforced concrete buildings

This study aims to build numerical model of retrofitting analysis of Reinforced Concrete (RC) buildings with vector form intrinsic finite element (VIFIFE). Fiber element method is introduced in the paper to acquire the moment-curvature relation of the section and to define the behaviour of RC material. This paper also uses equivalent truss to build the analysis model of brick wall. In the end, lab and in-situ test specimens are tilized to assess analysis method. The VFIFE method using in this study is one of the particle-based methods. The VFIFE method is included with the point value description, path element and convected material frame as the important characteristics. Thus, the RC structure is represented by finite particles. Each particle is subjected to the external forces and internal forces. The particle satisfies the Newton’s Law. A fictitious reversed rigid body motion is used to remove the rigid body motion from the deformations of the element. Pushover analysis is large deformation analysis. The conventional finite element method base program such as ETABS can encounter numerical difficulties in converging to a correct solution during an analysis involving large element deformation, highly non-linear plasticity or contact between surfaces. This research applied the VFIFE to solve the problems that usually occur on finite element program while performing pushover analysis. The analysis results show that for complex structures, VFIFE still could perform the pushover analysis until all the elements were collapsed but ETABS could not continue the analysis after reach the performance point. Automatically, VFIFE can predict better result in ductility rather than ETABS. As indicated in analysis results, better performance is identified in initial stiffness, yield strength, maximum strength, or steady strength of specimens when compared with lab test results. Biases are within the tolerance range. Additionally, when failure model of specimens is compared, similar performance with lab test result is found. Hence, analysis method proposed by this paper is able to effectively simulate seismic capacity and failure behaviour of RC buildings

4-(o-Tolyl­amino)­benzaldehyde

In the title compound, C14H13NO, the dihedral angle between the aromatic rings is 49.64 (18)°. The crystal structure is stabilized by N—H⋯O, C—H⋯O and C—H⋯π hydrogen bonds

Efficient non-uniform deblurring based on generalized additive convolution model

Image with non-uniform blurring caused by camera shake can be modeled as a linear combination of the homographically transformed versions of the latent sharp image during exposure. Although such a geometrically motivated model can well approximate camera motion poses, deblurring methods in this line usually suffer from the problems of heavy computational demanding or extensive memory cost. In this paper, we develop generalized additive convolution (GAC) model to address these issues. In GAC model, a camera motion trajectory can be decomposed into a set of camera poses, i.e., in-plane translations (slice) or roll rotations (fiber), which can both be formulated as convolution operation. Moreover, we suggest a greedy algorithm to decompose a camera motion trajectory into a more compact set of slices and fibers, and together with the efficient convolution computation via fast Fourier transform (FFT), the proposed GAC models concurrently overcome the difficulties of computational cost and memory burden, leading to efficient GAC-based deblurring methods. Besides, by incorporating group sparsity of the pose weight matrix into slice GAC, the non-uniform deblurring method naturally approaches toward the uniform blind deconvolution.Department of Computin

Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive

Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To correct RS distortions, existing methods adopt a fully supervised learning manner, where high framerate global shutter (GS) images should be collected as ground-truth supervision. In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions. In particular, a bidirectional distortion warping module is proposed for reconstructing dual reversed RS images, and then a self-supervised loss can be deployed to train DRSC network by enhancing the cycle consistency between input and reconstructed dual reversed RS images. Besides start and end RS scanning time, GS images at arbitrary intermediate scanning time can also be supervised in SelfDRSC, thus enabling the learned DRSC network to generate a high framerate GS video. Moreover, a simple yet effective self-distillation strategy is introduced in self-supervised loss for mitigating boundary artifacts in generated GS images. On synthetic dataset, SelfDRSC achieves better or comparable quantitative metrics in comparison to state-of-the-art methods trained in the full supervision manner. On real-world RS cases, our SelfDRSC can produce high framerate GS videos with finer correction textures and better temporary consistency. The source code and trained models are made publicly available at https://github.com/shangwei5/SelfDRSC. We also provide an implementation in HUAWEI Mindspore at https://github.com/Hunter-Will/SelfDRSC-mindspore.Comment: Accepted by ICCV 2023, available at https://github.com/shangwei5/SelfDRS