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.

神经隐式函数在计算机视觉、图形等多个领域具有重要意义，但缺乏全面数据集和大量计算资源限制了其发展和分析。为了解决这些挑战，我们引入了“隐式动物园”：一个需要数千个GPU训练天数的大规模数据集，旨在推动该领域的研究和开发。通过使用Implicit-Zoo，我们展示了两个直接好处：(1)学习变压器模型的令牌位置；(2)直接回归2D图像相对于NeRF模型的3D相机姿势。这进而提高了图像分类、语义分割和3D姿势回归三个任务的性能，为研究开辟了新的途径。

隐式动物园：用于2D图像和3D场景的大规模神经隐式函数数据集