Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenes
Qi Ma, Danda Pani Paudel, Ender Konukoglu, Luc Van Gool

TL;DR
Implicit-Zoo is a large-scale, high-quality dataset of neural implicit functions for 2D images and 3D scenes, designed to accelerate research and development in this computationally intensive field.
Contribution
The paper introduces Implicit-Zoo, a comprehensive dataset that addresses data scarcity and computational challenges in neural implicit function research.
Findings
Enables learning token locations for transformer models
Allows direct regression of 3D camera poses from 2D images
Improves performance in image classification, segmentation, and pose regression
Abstract
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,…
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Taxonomy
TopicsAdvanced Vision and Imaging
