Digital core reconstruction plays a vital role in subsurface analysis, but conventional techniques face challenges such as data scarcity, limited sample diversity, and the high cost of high-resolution CT imaging. Additionally, existing GAN-based models often suffer from geometric distortions and training instabilities, which compromise microstructural fidelity. To address these limitations, we propose a novel framework that integrates three key components: (1) StyleGAN3 for high-resolution image synthesis, (2) SIFT-based registration for precise structural alignment, and (3) a GAN-based inverse reconstruction pipeline for resolution enhancement. Evaluations on the Estaillades carbonate dataset demonstrate that our method achieves a 2.86-fold resolution improvement over conventional CT reconstruction, reducing reliance on high-end imaging equipment while preserving fine-scale microstructural details. This approach offers a scalable and cost-effective solution for digital rock analysis and reservoir simulation, advancing the state of the art in pore-scale modeling.

With the rapid advancement of computer vision and graphics, high-quality single-view 3D reconstruction has become increasingly significant in applications such as autonomous driving, robotics, and virtual reality. Traditional methods often struggle with the inherent ill-posed nature of single-view reconstruction, leading to limited accuracy and reduced detail retention. To address these challenges, we introduce an innovative framework that integrates an asymmetric U-Net architecture with 3D Gaussian splatting for single-view 3D reconstruction. Furthermore, our approach incorporates a novel 3D smoothing filter that constrains the maximum frequency of the 3D representation, effectively mitigating high-frequency artifacts in out-of-distribution rendering. By synergistically combining implicit and explicit representations, our method leverages the strengths of both to enhance reconstruction efficiency and quality. Experiments conducted on the SRN-Cars dataset demonstrate that our framework outperforms existing methods in quantitative metrics and qualitative assessments, achieving higher reconstruction accuracy and smoother surfaces.

This article presents a dual-branch Collaborative Siamese network architecture designed for visual object tracking, which we refer to as CoSiNet. The dual-branch collaborative Siamese network comprises two network branches: a shallow branch, which focuses on target localization to enhance resistance to interference from objects with similar characteristics, and a deep branch, which emphasizes the extraction of more abstract semantic information related to the object. Furthermore, we have devised a Channel Attention Feature Enhancement Module and a Spatial Channel Attention Feature Enhancement Module to augment feature extraction while mitigating the influence of background noise. In the concluding stages, an Adaptive Fusion Module is employed to amalgamate the response maps from both branches, resulting in an enhanced final response map. Experimental results, conducted on two publicly available datasets, demonstrate that our algorithm outperforms other state-of-the-art techniques in terms of tracking performance.

As human life continues improving, applications like virtual reality (VR) and augmented reality (AR) necessitate increasingly higher-quality 3D reconstructions. With the advancements in neural implicit 3D surface and volume rendering, multi-view 3D reconstruction has garnered significant attention. A prevailing issue in this domain is that the direct combination of neural implicit surface and volume rendering typically considers only photometric consistency loss, leading to an under-constrained surface problem. To address this problem, we develop a double-supervised approach and an integrated network for implicit surface and volume rendering, enabling the generation of a 3D surface model of an object from a set of multi-view images. In our approach, the object surface is represented by a signed distance function, and a 3D surface reconstruction model is jointly trained with photometric consistency constraints and geometry constraints. Experiments demonstrate that by enhancing prior geometry supervision and integrating the double-supervised network architecture into neural implicit 3D reconstruction, we can achieve accurate and high-quality 3D reconstruction.

Recent research suggests that virtual reality (VR) games can engage players in physical activity with high levels of enjoyment. Understanding users’ motivation to engage and enjoy immersive VR exercise platforms is thus important to designers. We designed a VR exercise platform and conducted an experiment with two conditions, one with a static user interface (UI) and the other with an open world environment. Across participants there was significantly (p = 0.03*) greater enjoyment reported in an open world compared to static UI. Enjoyment in both static UI and open world conditions was positively correlated wih user’s psychological needs and experience; autonomy and immersion. Participants’ future play intention was also predicted by autonomy and immersion, but only within the open world condition. Our findings also suggest players can be classified into entertainment-focused and exercise-focused with different expectations and therefore different engagement behaviors with each VR exercise environment. The study highlights the value of informing VR design with measures of psychological need satisfaction.

SUMMARY Exercise can improve health and well‐being. With this in mind, immersive virtual reality (VR) games are being developed to promote physical activity, and are generally evaluated through user studies. However, building such applications is time consuming and expensive. This paper introduces VR‐Rides, an object‐oriented application framework focused on the development of experiment‐oriented VR exergames. Following the modular programming pattern, this framework facilitates the integration of different hardware (such as VR devices, sensors, and physical activity devices) within immersive VR experiences that overlay game narratives on Google Street View panoramas. Combining software engineering and interaction patterns, modules of VR‐Rides can be easily added and managed in the Unity game engine. We evaluate the code efficiency and development effort across our VR exergames developed using VR‐Rides. The reliability, maintainability, and usability of our framework are also demonstrated via code metrics analysis and user studies. The results show that investing in a systematic approach to reusing code and design can be a worthwhile effort for researchers beyond software engineering.