论文标题
流神经场
Streamable Neural Fields
论文作者
论文摘要
神经领域已成为一种新的数据表示范式，并且在各种信号表示中都表现出色。由于它们在网络参数中保留信号，因此通过发送和接收整个模型参数来传输数据传输，从而阻止了在许多实际情况下使用该新兴技术。我们提出了流式神经场，这是一个由各种宽度的可执行子网络组成的单个模型。拟议的建筑和培训技术使一个网络可以随着时间的流逝而流式传输，并重建不同的素质和一部分信号。例如，较小的子网络会产生光滑且低频信号，而较大的子网络可以代表细节。实验结果表明，我们方法在各种域中的有效性，例如2D图像，视频和3D签名的距离函数。最后，我们证明我们提出的方法通过利用参数共享来提高培训稳定性。
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network parameters, the data transfer by sending and receiving the entire model parameters prevents this emerging technology from being used in many practical scenarios. We propose streamable neural fields, a single model that consists of executable sub-networks of various widths. The proposed architectural and training techniques enable a single network to be streamable over time and reconstruct different qualities and parts of signals. For example, a smaller sub-network produces smooth and low-frequency signals, while a larger sub-network can represent fine details. Experimental results have shown the effectiveness of our method in various domains, such as 2D images, videos, and 3D signed distance functions. Finally, we demonstrate that our proposed method improves training stability, by exploiting parameter sharing.
