Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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AFN: Attentional Feedback Network Based 3D Terrain Super-Resolution dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network based super-resolution

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Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Cloudsntation, many works approach these two tasks simultaneously and leverage the benefits of multi-task learning. However, most of them only considered simple strategies such as element-wise feature fusion, which may not lead to mutual promotion. In this work, we build a Bi-Directional Attention module

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Anatomy and Geometry Constrained One-Stage Framework for 3D Human Pose Estimationistency have not been well studied. In this work, to fully explore the priors on body structure and view-relationship for 3D human pose estimation, we propose an anatomy and geometry constrained one-stage framework. First of all, we define a kinematic structure model in deep learning framework which

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Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media volume. An image captured in scattering media degrades due to light scattering and attenuation caused by suspended particles. This degradation depends on scene depth; thus it is difficult for MVS to evaluate photometric consistency because the depth is unknown before three-dimensional reconstructio

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Homography-Based Egomotion Estimation Using Gravity and SIFT Features gravity vector. Using the information from an IMU, the .-axes of cameras can be aligned with the gravity, reducing their relative orientation to a single DOF (degree of freedom). In this paper, we use the gravity information to derive extremely efficient minimal solvers for homography-based egomoti

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https://doi.org/10.1007/978-3-030-69525-53d objects; artificial intelligence; biomedical image analysis; computer networks; computer vision; image