Learning Second-Order Attentive Context for Efficient Correspondence Pruning

Correspondence pruning aims to search consistent correspondences (inliers) from a set of putative correspondences. It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers. It's more challenging to ensure effectiveness while maintaining efficiency. In this paper, we propose an effective and efficient method for correspondence pruning. Inspired by the success of attentive context in correspondence problems, we first extend the attentive context to the first-order attentive context and then introduce the idea of attention in attention (ANA) to model second-order attentive context for correspondence pruning. Compared with first-order attention that focuses on feature-consistent context, second-order attention dedicates to attention weights itself and provides an additional source to encode consistent context from the attention map. For efficiency, we derive two approximate formulations for the naive implementation of second-order attention to optimize the cubic complexity to linear complexity, such that second-order attention can be used with negligible computational overheads. We further implement our formulations in a second-order context layer and then incorporate the layer in an ANA block. Extensive experiments demonstrate that our method is effective and efficient in pruning outliers, especially in high-outlier-ratio cases. Compared with the state-of-the-art correspondence pruning approach LMCNet, our method runs 14 times faster while maintaining a competitive accuracy.Comment: 9 pages, 8 figures; Accepted to AAAI 2023 (Oral

2-Dimensional Simulation of Deterioration Process for Life-cycle Performance Assessment of RC Structures in Marine Environment

The reliability-based durability design approach doesnt account for neither the surface deterioration of structures over service lives, nor the possible life-cycle maintenance. The paper employs the 2-dimentional (2D) simulation technique based on random field theory and Monte Carlo simulation method, to analyze the life-cycle performance of reinforced concrete structures under chloride attack, which is illustrated through the surface deterioration modelling of immersed tube tunnel segment of Hong Kong-Zhuhai-Macao (HZM) sea-link project. Then, the paper compares the maintenance demands imposed to different durability design specifications with different life-cycle performance target. The results may provide useful information in future durability design and aid the decision making process

Learning Probabilistic Coordinate Fields for Robust Correspondences

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know \textit{when and where to trust} the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty

Low-rank tensor completion (LRTC) is an important problem in computer vision and machine learning. The minimax-concave penalty (MCP) function as a non-convex relaxation has achieved good results in the LRTC problem. To makes all the constant parameters of the MCP function as variables so that futherly improving the adaptability to the change of singular values in the LRTC problem, we propose the bivariate equivalent minimax-concave penalty (BEMCP) theorem. Applying the BEMCP theorem to tensor singular values leads to the bivariate equivalent weighted tensor $\Gamma$-norm (BEWTGN) theorem, and we analyze and discuss its corresponding properties. Besides, to facilitate the solution of the LRTC problem, we give the proximal operators of the BEMCP theorem and BEWTGN. Meanwhile, we propose a BEMCP model for the LRTC problem, which is optimally solved based on alternating direction multiplier (ADMM). Finally, the proposed method is applied to the data restorations of multispectral image (MSI), magnetic resonance imaging (MRI) and color video (CV) in real-world, and the experimental results demonstrate that it outperforms the state-of-arts methods.Comment: arXiv admin note: text overlap with arXiv:2109.1225

Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS. In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error. Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane. To achieve it, we develop a coarse-to-fine framework called Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane. Technically, we predict two depth values for each pixel (Dual-Depth), and propose a novel loss function and a checkerboard-shaped selecting strategy to constrain the predicted depth geometry. Compared to existing methods,DMVSNet achieves a high rank on the DTU benchmark and obtains the top performance on challenging scenes of Tanks and Temples, demonstrating its strong performance and generalization ability. Our method also points to a new research direction for considering depth geometry in MVS.Comment: Accepted by ICCV 202

Vehicle Bridge Interaction Analysis on Concrete and Steel Curved Bridges

This study investigation is intended to research the dynamic response of horizontally curved bridges under heavy vehicle loads. Most of the main factors that affect the bridge dynamic response due to moving vehicles are considered. An improved 3D grid model, based on commercial software ANSYS Mechanical APDL, is developed for the analysis of curved bridges following the 3D shear-flexibility grillage analyzing method. A simplified numeric method, considering the effect of random road roughness and its velocity term, is developed for solving the interaction problem. With the model and numerical method presented, a series of parametric studies are conducted to study the curved bridge dynamic interaction. Based on the investigation of determining factors of curve bridge dynamic interaction, the expression of the upper-bound envelop for impact factors of maximum deflection is given with different surface conditions and highway speed limits as a function of bridge fundamental frequency or bridge central angle. A study is conducted on comparing these empirical equations and serval other major design codes, comments and suggestions are then made based on the discoveries

Fast Full-frame Video Stabilization with Iterative Optimization

Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.Comment: Accepted by ICCV202

Ultra‐high elastic strain energy storage in hybrid metal‐oxide infiltrated polymer nanocomposites

An understanding of the mechanical properties of materials at nanometer length scales, including a material’s ability to store and release elastic strain energy, is of great significance in the effective miniaturization of actuators, sensors and resonators for use in micro-/nano-electromechanical systems (MEMS/NEMS) as well as advanced development of artificial muscles for locomotion in soft robots. The measure of a material’s ability to store and release elastic strain energy, the modulus of resilience (R), is a crucial parameter in realizing such advanced mechanical actuation technologies. Typically, engineering a material system with a large R requires large increases in the material’s yield strength yet conservative increase in Young’s modulus, an engineering challenge as the two mechanical properties are strongly coupled; generally, strengthening methods results in considerable stiffening or increase in the Young’s modulus. Here, we present hybrid composite polymer nanopillars which achieve the highest specific R ever reported, by utilizing vapor-phase aluminum oxide infiltrations into lithographically patterned polymer resist SU-8. In-situ nanomechanical measurements reveal high, metallic-like yield strengths (~500 MPa) combined with a compliant, polymeric-like Young’s modulus (~7 GPa), a unique pairing never observed in known engineering materials. It is these exceptional elastic properties of our hybrid composite which allows for realization of R per density (Rs) values ~ 11200 J/kg, orders of magnitude greater than those in most engineering material systems. The high elastic energy storage/release capability of this material, as well as its compatibility with lithographic techniques, makes it an attractive candidate in the design of MEMS devices, which require an ultra-high elastic component for advanced actuation and sensor technologies. Furthermore, an opportunity for tunability of the elastic properties of the SU-8 polymeric material exists with this fabrication technique by varying the number of infiltration cycles or the organometallic precursor Please click Additional Files below to see the full abstract