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

Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D  Images and 3D Scenes

There has been a growing need to devise processes that can create
comprehensive datasets in the world of Computer Algebra, both for accurate
benchmarking and for new intersections with machine learning technology. We
present here a method to generate integrands that are guaranteed to be
integrable, dubbed the LIOUVILLE method. It is based on Liouville's theorem and
the Parallel Risch Algorithm for symbolic integration.
We show that this data generation method retains the best qualities of
previous data generation methods, while overcoming some of the issues built
into that prior work. The LIOUVILLE generator is able to generate sufficiently
complex and realistic integrands, and could be used for benchmarking or machine
learning training tasks related to symbolic integration.

我们提出了一种名为 LIOUVILLE 方法的生成可积函数的方法，它基于 Liouville 的定理和 Parallel Risch 算法，用于在计算代数中创建全面的数据集，并且能够产生复杂且逼真的可积函数，可用于符号积分的基准测试或机器学习训练任务。