mlsanityData to 7 Oct 2026

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

arXiv 2406.17438 ↗First posted 25 Jun 2024cs.CVcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 65 of its 77 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

65 of 77 references.

References · 65

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38 more
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 3.

12 papers.

  1. 2025SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretrainingcited by 51
  2. 2024ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretrainingcited by 21
  3. 2024HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolutioncited by 17
  4. 2024Fast Training of Sinusoidal Neural Fields via Scaling Initializationcited by 11
  5. 2025Optimizing Rank for High-Fidelity Implicit Neural Representationscited by 7
  6. 2025MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fieldscited by 6
  7. 2025Few-shot Implicit Function Generation via Equivariancecited by 2
  8. 2024PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detectioncited by 1
  9. 2025Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representationscited by 1
  10. 2026Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machinescited by 0
  11. 2026Is Class Signal Clustered or Routed in Task-Induced Implicit Neural Representation Weight Spaces?cited by 0
  12. 2026Weight-Space Mixture-of-Experts for Implicit Neural Representation Classificationcited by 0