Implicit Neural Representations (INR) or neural fields have emerged as a
popular framework to encode multimedia signals such as images and radiance
fields while retaining high-quality. Recently, learnable feature grids proposed
by Instant-NGP have allowed significant speed-up in the training as well as the
sampling of INRs by replacing a large neural network with a multi-resolution
look-up table of feature vectors and a much smaller neural network. However,
these feature grids come at the expense of large memory consumption which can
be a bottleneck for storage and streaming applications. In this work, we
propose SHACIRA, a simple yet effective task-agnostic framework for compressing
such feature grids with no additional post-hoc pruning/quantization stages. We
reparameterize feature grids with quantized latent weights and apply entropy
regularization in the latent space to achieve high levels of compression across
various domains. Quantitative and qualitative results on diverse datasets
consisting of images, videos, and radiance fields, show that our approach
outperforms existing INR approaches without the need for any large datasets or
domain-specific heuristics. Our project page is available at
this http URL .

SHACIRA 是一种简单但有效的通用框架，它通过在潜在空间中重参数化特征网格、应用熵正则化实现了对如图像、视频和辐射场等多种领域数据进行高水平压缩，超过了现有的 INR 方法，而无需大型数据集或特定领域的启发式算法。