We extend the Locally-Subdivided Neural Intersection Function (LSNIF) to support parameterized deformable and animated geometry. Our approach introduces a rest-space and deformed-space formulation inspired by meshless rendering, allowing ray samples to be mapped back to a canonical space where a single neural network represents geometry consistently across poses without retraining. To maintain accuracy under deformation-aware training, we incorporate scale-invariant distance regression, uncertainty-weighted multi-task learning, and a hybrid positional-grid encoding. The resulting method preserves the compactness and efficiency of LSNIF while enabling robust neural intersection prediction for dynamic geometry.

翻译：我们扩展了局部细分神经求交函数（LSNIF），以支持参数化可变形和动画几何体。我们的方法受无网格渲染启发，引入了一种“静止空间-形变空间”的数学表述，使得光线采样点可以被映射回一个规范空间，在该空间中，单个神经网络无需重新训练即可跨姿态一致地表示几何体。为了在形变感知训练过程中保持精度，我们结合了尺度不变距离回归、不确定性加权多任务学习以及一种混合位置-网格编码。所得方法保留了LSNIF的紧凑性和高效性，同时实现了对动态几何体的鲁棒神经求交预测。