Infrared Radiation (IR) images that capture the reflected IR signals from the surrounding environment have been applied to pedestrian detection and rescue tasks, especially for thermal images reflecting the surface temperature of the objects at nighttime. However, feature detection, extraction and matching are particularly challenging on thermal images as there are few textures compared to normal RGB images, which brings in an enormous gap in introducing various computer vision tasks such as image matching and retrieval, visual localization and SLAM. In this letter, we propose to design a full pipeline to learn a reliable and robust feature detector and descriptor. Our model trained on cross-spectral stereo and KAIST dataset is able to realize a reliable and accurate matching without further annotations. The proposed method is demonstrated to have superior performance compared with both widely used hand-crafted features and features extracted from the most recent learning-based models on visual and quantitative experiments. It also shows a good generalization and robustness ability on FLIR thermal benchmark even without training or fine-tuning on it.

In this paper, we develop a novel visual-inertial navigation system with motion constraint (VINS-Motion), which extends the visual-inertial navigation system (VINS) to incorporate vehicle motion information for improving the autonomous vehicles localization accuracy. We introduce the vehicle stop detection module in the pre-processing measurement procedure. During the back-end processing, if the vehicle is detected to be in a stopping state, we perform optimization based on the stop constraint to help eliminate the abnormal jitter of the estimated pose, thus ensuring the reasonability of the trajectory. Otherwise, besides the prior information, IMU measurement residual, and visual measurement residual utilized in VINS, vehicle orientation/velocity constraint built by Ackerman steering model is first exploited to constitute residuals. We minimize the sum of priors and Mahalanobis norms of three kinds of residuals to obtain a maximum posterior estimation, thus increasing system consistency and accuracy. The proposed approach is validated on public datasets and compared with state-of-the-art algorithms, which demonstrates that the motion constraint proposed in this paper improves the standard VINS performance and achieves significantly higher positioning accuracy.

If the accuracy of depth estimation from a single RGB image could be improved it would be possible to eliminate the need for expensive and bulky depth sensing hardware. The majority of efforts toward this end have been focused on utilizing geometric constraints, image sequences, or stereo image pairs with the help of a deep neural network. In this work, we propose a framework for simultaneous depth estimation from a single image and image focal stacks using depth-from-defocus and depth-from-focus algorithms. The proposed network is able to learn optimal depth mapping from the information contained in the blurring of a single image, generate a simulated image focal stack and all-in-focus image, and train a depth estimator from an image focal stack. As there is no large dataset specifically designed for our problem, we first learned on a synthetic indoor dataset: NYUv2. Then we compare the performance by comparing with other existing methods on DSLR dataset. Finally, we collected our own dataset using a DSLR and further verify on it. Experiments demonstrate that our system is able to provide comparable results compared with other state-of-the-art methods.

Plant roots are the main conduit to its interaction with the physical and biological environment. A 3D root system architecture can provide fundamental and applied knowledge of a plant's ability to thrive, but the construction of 3D structures for thin and complicated plant roots is challenging. Existing methods such as structure-from-motion and shape-from-silhouette require multiple images, as input, under a complicated optimization process, which is usually not convenient in fieldwork. Little effort has been put into investigating the applications of deep neural network methods to reconstruct thin objects, like plant root systems, from a single image. We propose an unsupervised learning scheme to estimate the root depth from only one image as input, which is further applied to reconstruct the complete root system. The boundaries of the reconstructed object usually contain large errors, which is a significant problem for roots with many thin branches. To reduce reconstruction errors, we integrate a cross-view GAN-based network into the reconstruction process, which predicts the root image from a different perspective. Based on the predicted view, we reconstruct the root system using stereo reconstruction, which helps to identify the accurately reconstructed points by enforcing their consistency. The results on both the real plant root dataset and the synthetic dataset demonstrate the effectiveness of the proposed algorithm compared with state-of-the-art single image 3D reconstruction models on plant roots.

Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. Compared to conventional AI models, typically designed for a limited range of tasks, demand significant amounts of domain-specific data for training and may not always consider intricate interpersonal dynamics in education. AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions. This position paper reviews AGI's key concepts, capabilities, scope, and potential within future education, including achieving future educational goals, designing pedagogy and curriculum, and performing assessments. It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures. AGI systems can adapt to individual student needs, offering tailored learning experiences. They can also provide comprehensive feedback on student performance and dynamically adjust teaching methods based on student progress. The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions, which are critical in educational settings. The paper discusses that ethical issues in education with AGI include data bias, fairness, and privacy and emphasizes the need for codes of conduct to ensure responsible AGI use in academic settings like homework, teaching, and recruitment. We also conclude that the development of AGI necessitates interdisciplinary collaborations between educators and AI engineers to advance research and application efforts.