We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and density. Sample distribution and density invariance enables HDFE to consistently encode continuous objects regardless of their sampling, and therefore allows neural networks to receive continuous objects as inputs for machine learning tasks, such as classification and regression. Besides, HDFE does not require any training and is proved to map the object into an organized embedding space, which facilitates the training of the downstream tasks. In addition, the encoding is decodable, which enables neural networks to regress continuous objects by regressing their encodings. Therefore, HDFE serves as an interface for processing continuous objects. We apply HDFE to function-to-function mapping, where vanilla HDFE achieves competitive performance as the state-of-the-art algorithm. We apply HDFE to point cloud surface normal estimation, where a simple replacement from PointNet to HDFE leads to immediate 12% and 15% error reductions in two benchmarks. In addition, by integrating HDFE into the PointNet-based SOTA network, we improve the SOTA baseline by 2.5% and 1.7% in the same benchmarks.

翻译：我们提出超维函数编码（HDFE）。给定连续对象（如函数）的样本，HDFE生成该对象的显式向量表示，该表示对样本分布和密度具有不变性。样本分布与密度不变性使得HDFE能够一致地编码连续对象，无论其采样方式如何，因此允许神经网络接收连续对象作为机器学习任务（如分类与回归）的输入。此外，HDFE无需任何训练，并被证明可将对象映射至有组织的嵌入空间，从而促进下游任务的训练。同时，该编码具备可解码性，使得神经网络能够通过回归对象的编码来回归连续对象。因此，HDFE可作为处理连续对象的接口。我们将HDFE应用于函数到函数映射，其中原始HDFE取得了与最先进算法相当的性能。我们还将HDFE应用于点云表面法向估计，在两项基准测试中，将PointNet简单替换为HDFE即可直接降低12%和15%的误差。此外，通过将HDFE集成至基于PointNet的最先进网络，我们在相同基准测试中将最优基线分别提升了2.5%和1.7%。