For some patients with medically refractory epilepsy (MRE), surgery is a safe and effective treatment for controlling epilepsy. However, the functional consequences of such surgery on brain activity and connectivity in children remain unknown. In the present study, we carried out a longitudinal study using resting state fMRI in ten children with MRE before and again at a mean of 79 days after surgery, as well as in a group of 28 healthy controls. Compared to the controls, children with epilepsy exhibited abnormalities in intrinsic activity in the thalamus, putamen, pallidum, insula, hippocampus, cerebellum and cingulate gyrus both before and after surgery. Longitudinal analyses showed that the amplitude of low frequency fluctuations (ALFF) increased in the parietal-frontal cortex and decreased in the deep nuclei from pre- to post-surgery. The percentage changes in ALFF values in the deep nuclei were positively correlated with the age of epilepsy onset. Functional connectivity (FC) analyses demonstrated a reorganization of FC architecture after surgery. These changes in brain activity and FC after surgery might indicate that the previously disrupted functional interactions were reorganized after surgery. All these results provide preliminary evidence that the age of epilepsy onset may have some potential to predict the outcome of brain functional reorganization after surgery in children with MRE.

Recent advances in 3D Gaussian Splatting (3DGS) have enabled RGB-only SLAM systems to achieve high-fidelity scene representation. However, the heavy reliance of existing systems on photometric rendering loss for camera tracking undermines their robustness, especially in unbounded outdoor environments with severe viewpoint and illumination changes. To address these challenges, we propose TVG-SLAM, a robust RGB-only 3DGS SLAM system that leverages a novel tri-view geometry paradigm to ensure consistent tracking and high-quality mapping. We establish temporally consistent tri-view correspondences to enable three core contributions. First, Hybrid Geometric Constraints combine geometric cues (trifocal and 3D alignment) with photometric supervision, ensuring stable pose estimation when illumination shifts cause rendering inconsistencies. Second, TUGI estimates uncertainty from multi-view 3D geometric consistency—capturing cross-view stability rather than pairwise 2D matching confidence—to guide principled Gaussian initialization. Third, DART adaptively attenuates photometric trust when rendering degrades, allowing geometric priors to govern optimization. Experiments on multiple public outdoor datasets show that TVG-SLAM outperforms prior RGB-only 3DGS-based SLAM systems. Notably, in the most challenging dataset, our method reduces the average ATE by 69.0% while achieving state-of-the-art rendering quality.

Accurate and reliable estimation of biases of low-cost Inertial Measurement Units (IMU) is a key factor to maintain the resilience of Visual-Inertial Odometry (VIO), particularly when visual tracking fails in challenging areas. To address the issue of inaccurate estimation of low-cost IMU bias resulting from visual tracking failures, we propose an Inertial Prior Network (IPNet), a plug-and-play module that captures platform-specific motion characteristics directly from raw IMU measurements to infer bias priors. This approach eliminates the dependency on recursive bias estimation combining visual features, thus effectively preventing error propagation in challenging areas. Additionally, to compensate for the scarcity of ground-truth bias in most visual-inertial datasets, we introduce an iterative method to compute the mean IMU bias for each sequence to facilitate network training. Extensive experimental results on the EuRoC and TumVi public datasets, as well as an in-house dataset, demonstrate that the IPNet significantly enhances localization precision and robustness. Specifically,on the public benchmarks, the average improvements in ATE-RMSE and RPE-RMSE reached 46% and 48%, respectively. Moreover, the model’s cross-scene generalization is confirmed by successfully applying the indoor-trained prior network to outdoor autonomous driving scenarios. The source code is available at: https://github.com/yiyscut/VIO-IPNet.git.

Structure-from-motion (SfM) is a foundational technology that facilitates 3D scene understanding and visual localization. However, bundle adjustment (BA)-based SfM is usually very time-consuming, especially when dealing with numerous unknown focal length cameras. To address these limitations, we proposed a novel SfM system based on pose-only adjustment (PA) for intrinsic and extrinsic joint optimization to accelerate computing. Firstly, we propose a base frame selection method based on depth uncertainty, which integrates the focal length and parallax angle under a multi-camera system to provide more stable depth estimation for subsequent optimization. We explicitly derive a global PA of joint intrinsic and extrinsic parameters to reduce the high dimensionality of the parameter space and deal with cameras with unknown focal lengths, improving the efficiency of optimization. Finally, a novel pose-only re-triangulation (PORT) mechanism is proposed for enhanced reconstruction completeness by recovering failed triangulations from incomplete point tracks. The proposed framework has been demonstrated to be both faster and comparable in accuracy to state-of-the-art SfM systems, as evidenced by public benchmarking and analysis of the visitor photo dataset.