Masked Autoencoders in 3D Point Cloud Representation Learning

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, self-supervised learning based upon masking local surface patches for 3D point cloud data has been under-explored. In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D Transformers to learn both local features of point cloud patches and high-level contextual relationships between patches and complete the latent representations of masked patches. We use our Point Cloud Reconstruction Module with multi-task loss to complete the incomplete point cloud as a result. We conduct self-supervised pre-training on ShapeNet55 with the point cloud completion pre-text task and fine-tune the pre-trained model on ModelNet40 and ScanObjectNN (PB\_T50\_RS, the hardest variant). Comprehensive experiments demonstrate that the local features extracted by our MAE3D from point cloud patches are beneficial for downstream classification tasks, soundly outperforming state-of-the-art methods ($93.4\%$ and $86.2\%$ classification accuracy, respectively).Comment: Accepted to IEEE Transactions on Multimedi

3D sunken relief generation from a single image by feature line enhancement

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient

Finite Element Analysis of Precast Concrete Deck-Steel Beam-Connection Concrete (PCSC) Connectors Using Ultra-High Performance Concrete (UHPC) for the Composite Beam

A precast concrete deck-steel beam-connection concrete (PCSC) connector using ultra-high-performance concrete (UHPC) as post-cast concrete has been proposed to enable rapid on-site construction of assembled composite bridges. This paper aims to optimize the structure of PCSC connectors using the finite element (FE) model to maximize material utilization and economic efficiency. A refined FE model comprising the bond degradation at the steel–UHPC interface was developed based on the push-out experimental results of PCSC connectors. The shear mechanism of the PCSC connectors was analyzed. Subsequently, parametric analyses were performed to investigate the effects of stud diameter, height, spacing, and concrete strength on the mechanical properties of PCSC connectors. The results indicate that the bond at the steel–connection concrete interface positively affects the shear bearing capacity and stiffness of the PCSC connectors. When UHPC was used as connection concrete, it improved the bearing capacity by about 20% and the shear stiffness of the stud by about 16% compared with normal concrete, but the ductility was 38% lower. It was also found that increasing the compressive strength of the connection concrete increased the shear strength of specimens. However, when the compressive strength of UHPC exceeded 130 MPa, the additional UHPC strength did not significantly enhance the shear performance of specimens. In order to ensure the effective restraint of the connection concrete to studs, it is recommended that the minimum width and height of the connection concrete (UHPC) be determined based on the minimum horizontal spacing and height of the studs. Specifically, the length-to-diameter ratio of studs is greater than or equal to 3.18, the horizontal spacing of studs can be at least 2.82 d, and the clear distance between the outer stud shank and the edge of the UHPC cannot be less than 30 mm. The results are expected to provide a reference for the engineering design of PCSC connectors and a reference for conventional stud connectors with UHPC

Study on the Static Performance of Prefabricated UHPC-Steel Epoxy Bonding Interface

Prefabricated UHPC-steel composite structure can make full use of the two materials’ mechanical and construction performance characteristics, with super mechanical properties and durability, which has been proved to be a very promising structure. However, using traditional mechanical connectors to connect prefabricated UHPC and steel not only is inconvenient for the prefabrication of UHPC components but also introduces heavy welding work, which is detrimental to the construction speed and antifatigue performance of the composite structure. Bonding UHPC-steel interface with epoxy adhesive is a potential alternative to avoid the above problem. In order to explore the mechanical properties of the prefabricated UHPC-steel epoxy bonding interface, this study carried out the direct shear test, tensile test, and tensile-shear test of the UHPC-steel epoxy-bonded interface (prefabricated UHPC-steel epoxy bonding interface). The results show that the interface failure is mainly manifested as the peeling of the epoxy-UHPC interface and the destruction of part of the UHPC matrix (the failure of the UHPC's surface). In pure shear and pure tension state, the interfacial shear strength is 5.14 MPa and the interfacial tensile strength is 1.18 MPa. In the tensile-shear state, the interfacial shear strength is 0.61 MPa and the interfacial tensile strength is 1.06 MPa. The stress-displacement curves of the interface normal and tangential direction are all in the shape of a two-fold line. The ultimate displacement was within 0.1 mm, showing the characteristics of brittle failure. Finally, a numerical model of the tensile specimen is established based on the cohesive interface element, and the interfacial tensile-shear coupling failure mechanism (tensile-shear coupling effect) is analyzed

Masked autoencoders in 3d point cloud representation learning

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, methods based upon masking Autoencoders have been explored in the fields. The input can be intuitively masked due to regular content, like sequence words and 2D pixels. However, the extension to 3D point cloud is challenging due to irregularity. In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D Transformers to learn both local features of point cloud patches and high-level contextual relationships between patches, then complete the latent representations of masked patches. We use our Point Cloud Reconstruction Module with multi-task loss to complete the incomplete point cloud as a result. We conduct self-supervised pre-training on ShapeNet55 with the point cloud completion pre-text task and fine-tune the pre-trained model on ModelNet40 and ScanObjectNN (PB_T50_RS, the hardest variant). Comprehensive experiments demonstrate that the local features extracted by our MAE3D from point cloud patches are beneficial for downstream classification tasks, soundly outperforming state-of-the-art methods (93.4% and 86.2% classification accuracy, respectively). Our source codes are available at: https://github.com/Jinec98/MAE3D

Experimental and Numerical Investigation of the Shear Performance of PSCC Shear Connectors with Poured UHPC

Assembled steel-composite bridges generally use stud connectors to achieve the connection between the deck slab and the steel main girders. However, the commonly-used cluster studs weaken the integrity of the precast deck slabs and are not conducive to reducing the size of the precast deck slabs. Based on the excellent mechanical performance of UHPC, a precast steel-concrete composite bridge system consisting of precast bridge deck slabs, bonding cavities, and steel girders was proposed in this study. The system was named PSCC (Precast Steel-Concrete Connectors). To verify the applicability of PSCC connectors in engineering, push-out tests and finite element analysis were carried out in this paper to investigate the shear performance and influence parameters of PSCC connectors. The results showed that compared with the full bonding at the steel-UHPC interface, the shear bearing capacity of the specimens with 30% debonded area rate (the ratio of defect area to total interface area) and the shear bearing capacity of the specimens with 60% debonded area rate decreased by 0.35% and 9.74%, the elastic stiffness decreased by 14.86% and 21.72%, and the elastic-plastic stiffness decreased by 1.6% and 12.8%, respectively. When the steel-UHPC percentage of debonded area is less than 30%, the shear resistance of PSCC connectors is affected very little. However, when the steel-UHPC percentage of debonded area is 60%, the shear resistance of PSCC connectors is greatly affected. Therefore, adequate filling of the UHPC connection layer should be ensured in the project. In addition, the PSCC connectors have excellent ductility, their characteristic slip value Su is much higher than the EC4 specification of 6 mm, and they have better shear performance than conventionally installed stud connectors. According to the results of the parametric analysis, it was found that the failure mode of the PSCC connectors was shear reinforcement fracture when the area ratio of shear reinforcement to stud was less than 1.55, under the premise of the same material strength. On the contrary, the failure mode of PSCC connections was stud fracture. When the transverse spacing of both studs and shear reinforcement is 4d, the PSCC connectors can maintain a high ultimate load capacity while reducing the amount of UHPC in the bonding cavity. Therefore, 4d was chosen as the best spacing for both studs and shear reinforcement