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Objaverse-XL: A Universe of 10M+ 3D Objects (2307.05663v1)

Published 11 Jul 2023 in cs.CV and cs.AI

Abstract: Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data. However, 3D vision tasks have not seen the same progress, in part due to the challenges of acquiring high-quality 3D data. In this work, we present Objaverse-XL, a dataset of over 10 million 3D objects. Our dataset comprises deduplicated 3D objects from a diverse set of sources, including manually designed objects, photogrammetry scans of landmarks and everyday items, and professional scans of historic and antique artifacts. Representing the largest scale and diversity in the realm of 3D datasets, Objaverse-XL enables significant new possibilities for 3D vision. Our experiments demonstrate the improvements enabled with the scale provided by Objaverse-XL. We show that by training Zero123 on novel view synthesis, utilizing over 100 million multi-view rendered images, we achieve strong zero-shot generalization abilities. We hope that releasing Objaverse-XL will enable further innovations in the field of 3D vision at scale.

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An Analysis of Objaverse-XL: A Landmark Dataset for 3D Vision

Introduction

The field of artificial intelligence has experienced significant advancements, particularly driven by large datasets facilitating breakthrough improvements in language and image models. However, 3D vision has lagged due to the scarcity of comprehensive, high-quality datasets. To address this gap, "Objaverse-XL: A Universe of 10M+ 3D Objects" introduces an extensive 3D dataset that aims to propel 3D vision research to the level of its 2D counterparts. This paper presents Objaverse-XL, a dataset containing over 10 million deduplicated 3D objects from a diverse range of sources, thus offering unprecedented scale and diversity in 3D datasets. This analysis provides insights into the dataset's composition, its benefits for current 3D vision advancements, its applications, and future research implications.

Dataset Composition and Sources

Objaverse-XL aggregates 3D assets from a multitude of sources such as GitHub, Thingiverse, Sketchfab, Polycam, and the Smithsonian Institution. This diversity encompasses manually designed objects as well as data acquired via photogrammetry. It represents an expansion over previous datasets like Objaverse 1.0 and ShapeNet, offering more than ten times the volume of the former. Each 3D object within Objaverse-XL includes metadata such as file size, polygon count, and rendering views, facilitating a comprehensive understanding of the dataset's scope.

Methodology and Experiments

A primary focus of this paper is using Objaverse-XL to improve novel view synthesis, demonstrated through its integration into models like Zero123-XL and PixelNeRF. Experimentation shows pronounced enhancements in zero-shot generalization and scene understanding tasks when using Objaverse-XL as a pretraining corpus. For instance, Zero123-XL, fine-tuned with Objaverse-XL, outperforms earlier versions by generating more accurate and diverse novel views, capitalizing on the rich variety of the dataset. Such improvements underscore the potential of Objaverse-XL to enable more sophisticated training paradigms across 3D vision tasks.

Implications and Applications

The practical implications of Objaverse-XL are substantial, particularly for augmenting 3D model training and validation. In robotics, AR/VR, and graphics, access to such a large-scale dataset can drive advancements in applications requiring realistic 3D simulations. The dataset invites exploration into 3D object generation, reconstruction, and context-aware 3D scene understanding, potentially allowing AI to seamlessly integrate with real-world applications. Moreover, Objaverse-XL's ability to enhance model generalization to previously unseen 3D modalities—like anime or sketches—paves the way for more aligned and versatile AI applications.

Future Directions

While Objaverse-XL sets a new benchmark, future research may factor in further scaling, facilitating the transition from handcrafted data to web-crawled, diverse sources. Moreover, the exploration of selective data utilization, by understanding the inherent quality or relevance of 3D objects, can optimize model training efficiency. The paper also suggests the necessity of continued development in automated deduplication and data curation techniques given the dataset's scale. On a theoretical front, this work invites rethinking the architectural and algorithmic designs that can leverage such massive datasets effectively, potentially foreshadowing new learning paradigms in 3D AI.

Conclusion

"Objaverse-XL: A Universe of 10M+ 3D Objects" represents a significant leap forward for 3D vision research by providing a massive, diverse dataset, which empowers advanced AI models to perform complex 3D tasks with improved generalizability and versatility. The breadth of Objaverse-XL not only fuels progress in existing applications but opens avenues for new innovations in technology and AI. Given the dataset's potential to reshape 3D vision, its impact will likely reverberate across academia and industry, setting the stage for a new era in 3D understanding and applications.

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Authors (17)
  1. Matt Deitke (11 papers)
  2. Ruoshi Liu (17 papers)
  3. Matthew Wallingford (13 papers)
  4. Huong Ngo (2 papers)
  5. Oscar Michel (8 papers)
  6. Aditya Kusupati (28 papers)
  7. Alan Fan (4 papers)
  8. Christian Laforte (3 papers)
  9. Vikram Voleti (25 papers)
  10. Samir Yitzhak Gadre (12 papers)
  11. Eli VanderBilt (10 papers)
  12. Aniruddha Kembhavi (79 papers)
  13. Carl Vondrick (93 papers)
  14. Georgia Gkioxari (39 papers)
  15. Kiana Ehsani (31 papers)
  16. Ludwig Schmidt (80 papers)
  17. Ali Farhadi (138 papers)
Citations (268)
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