Neural Radiance Fields (NeRF) face significant challenges in extreme few-shot scenarios, primarily due to overfitting and long training times. Existing methods, such as FreeNeRF and SparseNeRF, use frequency regularization or pre-trained priors but struggle with complex scheduling and bias. We introduce FrugalNeRF, a novel few-shot NeRF framework that leverages weight-sharing voxels across multiple scales to efficiently represent scene details. Our key contribution is a cross-scale geometric adaptation scheme that selects pseudo ground truth depth based on reprojection errors across scales. This guides training without relying on externally learned priors, enabling full utilization of the training data. It can also integrate pre-trained priors, enhancing quality without slowing convergence. Experiments on LLFF, DTU, and RealEstate-10K show that FrugalNeRF outperforms other few-shot NeRF methods while significantly reducing training time, making it a practical solution for efficient and accurate 3D scene reconstruction.

Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360° unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360° unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes.

LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gaussians to avoid local minima and ensure global consistency; (2) a robust Pose Estimation Module leveraging learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that converts dense point clouds into anchors based on spatial density. Extensive experiments on challenging benchmarks demonstrate that LongSplat achieves state-of-the-art results, substantially improving rendering quality, pose accuracy, and computational efficiency compared to prior approaches. Project page: https://linjohnss.github.io/longsplat/

The secure requirement of modern information systems is increasing significantly nowadays, especially in cloud computing with distributed applications. Among secure mechanisms of an organization, the access control (AC) is a foundation for modern information systems. In order to reach an effective and flexible approach of access control, the Role-based Access Control (RBAC) models are widely deployed in organizations. Comparing with traditional discretionary access control (DAC) and mandatory access control (MAC), the user-role-subject mapping of a RBAC model provides effective permissions assignments for access control of an organization. However, the RBAC sessions could be stretched over multiple distributed servers in cloud computing. The traversed sessions between servers could be modified, monitored and attacked by modern hacker techniques, and become secure leaks in RBAC models. In order to ensure secure sessions in cloud computing, various encryption approaches are used. Among these encryption approaches, the use of digital certificates by asymmetric encryption methods could be an appropriate solution to ensure the security of RBAC sessions. However, the cost of public/private keys management and issuing an appropriate certificate tokens for each member in organizations could be very expensive. The high cost might block the deployment of secure RBAC sessions, and then reduce the secure level of organizations. In order to improve this issue, a low-cost approach of secure sessions for RBAC models is proposed in this paper. The personal smart cards can be used as a certificate tokens in RBAC models to reach effective user authentications. Moreover, each session of RBAC models, including user-role-subject assignments and content-based accesses, can be protected by digital certificates which is generated by user own smart cards. Thus the security of RBAC sessions can be improved significantly. It is worth noting that personal smart cards are issued by public departments, thus the expense of tokens issuing and key management could be minimized. Therefore, the session security of a RBAC model could be ensured with user own smart cards without additional cost.