Objaverse: A Universe of Annotated 3D Objects
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Cited by 349 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.
Key facts. Cited by 1,522 papers in the map (data through 7 Oct 2026). Builds on 72 of its 88 references that are in the map. First posted on arXiv 15 Dec 2022.
Builds on
72 of 88 references.
References · 72
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50 most cited of 72 All 72 (JSON)
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Citing papers per year: 2022: 2; 2023: 178; 2024: 473; 2025: 498; 2026: 371.
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