  Neural implicit functions have demonstrated significant importance in various
areas such as computer vision, graphics. Their advantages include the ability
to represent complex shapes and scenes with high fidelity, smooth interpolation
capabilities, and continuous representations. Despite these benefits, the
development and analysis of implicit functions have been limited by the lack of
comprehensive datasets and the substantial computational resources required for
their implementation and evaluation. To address these challenges, we introduce
"Implicit-Zoo": a large-scale dataset requiring thousands of GPU training days
designed to facilitate research and development in this field. Our dataset
includes diverse 2D and 3D scenes, such as CIFAR-10, ImageNet-1K, and
Cityscapes for 2D image tasks, and the OmniObject3D dataset for 3D vision
tasks. We ensure high quality through strict checks, refining or filtering out
low-quality data. Using Implicit-Zoo, we showcase two immediate benefits as it
enables to: (1) learn token locations for transformer models; (2) directly
regress 3D cameras poses of 2D images with respect to NeRF models. This in turn
leads to an improved performance in all three task of image classification,
semantic segmentation, and 3D pose regression, thereby unlocking new avenues
for research.
