Efficient and high-fidelity 3D scene modeling is a long-standing pursuit in computer graphics. While recent 3D Gaussian Splatting (3DGS) methods achieve impressive real-time modeling performance, they rely on resource-unconstrained training assumptions that fail on mobile devices, which are limited by minute-scale training budgets and hardware-available peak-memory. We present PocketGS, a mobile scene modeling paradigm that enables on-device 3DGS training under these tightly coupled constraints while preserving high perceptual fidelity. Our method resolves the fundamental contradictions of standard 3DGS through three co-designed operators: G builds geometry-faithful point-cloud priors; I injects local surface statistics to seed anisotropic Gaussians, thereby reducing early conditioning gaps; and T unrolls alpha compositing with cached intermediates and index-mapped gradient scattering for stable mobile backpropagation. Collectively, these operators satisfy the competing requirements of training efficiency, memory compactness, and modeling fidelity. Extensive experiments demonstrate that PocketGS is able to outperform the powerful mainstream workstation 3DGS baseline to deliver high-quality reconstructions, enabling a fully on-device, practical capture-to-rendering workflow.

翻译：高效且高保真的3D场景建模是计算机图形学长期追求的目标。尽管近期3D高斯溅射（3DGS）方法在实时建模性能上取得了显著成果，但这些方法依赖于无资源约束的训练假设，无法在受限于分钟级训练预算和硬件峰值内存的移动设备上运行。我们提出PocketGS——一种移动场景建模范式，能够在这些紧耦合约束下实现设备端3DGS训练，同时保持高感知保真度。该方法通过三种协同设计的算子解决标准3DGS的根本性矛盾：G算子构建几何保真的点云先验；I算子注入局部表面统计量以播种各向异性高斯体，从而缩小早期条件差距；T算子结合缓存中间结果与索引映射梯度散射展开阿尔法合成，实现稳定的移动端反向传播。这些算子共同满足了训练效率、内存紧凑性与建模精度之间的竞争性需求。大量实验表明，PocketGS能够超越强大的主流工作站级3DGS基线，生成高质量重建结果，实现完全设备端、从采集到渲染的实用化工作流程。