Objaverse-XL: A Universe of 10M+ 3D Objects
Objaverse-XL, with over 10 million 3D models, significantly advances large-scale 3D vision tasks.
Key Findings
Methodology
The study constructs Objaverse-XL by aggregating and deduplicating over 10 million 3D models from sources like GitHub, Sketchfab, Polycam, and Smithsonian. High-quality models are rendered from multiple viewpoints, generating over 100 million images used to train Zero123-XL via diffusion and NeRF architectures. CLIP ViT-L/14 features guide data filtering and model conditioning. The dataset's diversity enables models to generalize across complex scenes and styles, validated through zero-shot view synthesis experiments. The approach integrates multi-source data processing, quality control, and large-scale rendering, establishing a new benchmark for 3D pretraining.
Key Results
- Zero123-XL achieves a PSNR of 19.876, SSIM 0.888, LPIPS 0.075, and FID surpassing previous models on Google Scanned Objects, demonstrating superior zero-shot view synthesis. Training on over 10 million models leads to consistent performance improvements across categories, especially in complex and textured scenes.
- In NeRF and PixelNeRF tasks, models pretrained on Objaverse-XL outperform baselines by approximately 2 PSNR points, indicating enhanced 3D reconstruction and novel view synthesis capabilities.
- Analysis reveals improved model robustness to sparse scans and textured artifacts, with notable gains in antique and handcrafted object representations, confirming the dataset's diversity benefits.
Significance
This work addresses the critical bottleneck of limited 3D data by providing an unprecedentedly large and diverse dataset, enabling the development of more generalizable and detailed 3D models. It paves the way for breakthroughs in AR/VR, robotics, and content creation, reducing reliance on costly manual modeling. The dataset's scale fosters research into zero-shot and few-shot learning, bridging the gap between 2D and 3D AI capabilities. It also sets a new standard for multi-source data integration in 3D deep learning, promising rapid advancements in the field.
Technical Contribution
The paper introduces a comprehensive pipeline for large-scale 3D data collection, deduplication, and quality filtering, combined with multi-view rendering and CLIP-based feature extraction. It innovates by training the first diffusion-based 3D view synthesis model, Zero123-XL, on a dataset exceeding 10 million models, significantly improving zero-shot generalization. The integration of multi-source data, along with novel filtering techniques, enhances model robustness and diversity. The architecture combines diffusion models with NeRF, enabling detailed and realistic novel view generation across diverse styles and textures.
Novelty
This is the first effort to assemble a multi-source, multi-modal 3D dataset of this scale, surpassing prior datasets like ShapeNet and Objaverse-1.0 by an order of magnitude. The combination of large-scale multi-view rendering, CLIP feature-guided filtering, and diffusion-based view synthesis represents a novel approach that significantly advances zero-shot 3D generation. The dataset's diversity and quality set new benchmarks for pretraining large 3D models, opening avenues for research previously limited by data scarcity.
Limitations
- Despite its scale, Objaverse-XL remains smaller than billion-scale image-text datasets, limiting some zero-shot capabilities. Some models suffer from incomplete textures or geometric inaccuracies, affecting downstream tasks.
- High computational costs for rendering, filtering, and training restrict widespread adoption. The dataset may contain biases inherited from web sources, impacting model fairness and robustness.
- Handling of low-quality or sparse scans remains challenging, requiring further refinement in data processing and model architecture to improve detail fidelity and generalization.
Future Work
Future efforts will focus on expanding dataset size through automated web crawling and improved filtering, enhancing data quality, and reducing biases. Developing more efficient training architectures will lower resource barriers. Integrating multimodal data, such as text and images, will enable richer scene understanding. Exploring semi-supervised and active learning strategies could further improve model robustness. Additionally, applying these models to real-world applications like robotics, AR/VR content creation, and digital heritage preservation will be prioritized.
AI Executive Summary
The rapid progress of AI in language and 2D vision has been driven by large-scale datasets, enabling models like GPT-4 and CLIP to achieve remarkable capabilities. However, in 3D vision, progress has lagged due to a scarcity of extensive, high-quality data. Traditional datasets such as ShapeNet and Objaverse-1.0, while influential, are limited in scale and diversity, constraining the development of generalizable 3D models.
Recognizing this bottleneck, the authors introduce Objaverse-XL, a massive dataset comprising over 10 million deduplicated 3D models sourced from the internet, including repositories like GitHub, Sketchfab, Polycam, and Smithsonian. This dataset captures a broad spectrum of object categories, styles, and complexities, providing a rich foundation for training large-scale 3D models. The construction involved meticulous filtering, multi-view rendering, and feature extraction using CLIP, ensuring high-quality, diverse data.
Leveraging this dataset, the team trained Zero123-XL, a diffusion-based model capable of novel view synthesis. Experiments demonstrated that Zero123-XL surpasses previous models in zero-shot generalization, achieving a PSNR of 19.876 and SSIM of 0.888 on Google Scanned Objects, with notable improvements across categories such as antiques, cartoons, and sketches. The integration of multi-source data and advanced rendering techniques significantly enhances the model's ability to generate realistic, diverse 3D views from limited input.
This work marks a substantial leap forward in 3D AI, addressing longstanding data limitations and opening new avenues for applications in AR/VR, robotics, digital heritage, and content creation. The authors highlight ongoing challenges, including computational costs and data biases, and outline future directions such as dataset expansion, improved filtering, and multimodal integration. Overall, Objaverse-XL sets a new benchmark for large-scale 3D pretraining, promising to accelerate innovations across both academia and industry.
Deep Dive
Glossary
Diffusion Model (扩散模型)
一种生成模型，通过逐步去噪实现高质量图像或3D内容的生成。In this paper, used for view synthesis.
Zero123-XL采用扩散模型进行新视角生成。
Neural Radiance Field (NeRF, 神经辐射场)
一种表示3D场景的连续体积渲染技术，能从少量视角合成新视图。用于3D重建和渲染。
结合NeRF实现多视角一致性。
CLIP (Contrastive Language-Image Pretraining, 对比语言-图像预训练)
一种多模态模型，能将图像和文本映射到共同空间，用于特征提取和过滤。
筛选高质量模型和描述。
Zero-shot Learning (零样本学习)
模型在未见过的类别或任务上表现出良好的泛化能力。
Zero123-XL在新视角合成中的应用。
多视角渲染 (Multi-view Rendering)
从不同角度生成对象图像，用于训练和评估模型。
生成超过1亿图像丰富训练数据。
Open Questions Unanswered questions from this research
- 1 如何进一步提升大规模3D模型的纹理和几何细节还原能力，仍需探索更高效的模型架构和数据处理技术。
- 2 现有数据偏向互联网公开资源，可能存在偏差，未来需开发偏差校正和多样性增强方法。
Applications
Immediate Applications
虚拟内容生成
利用大规模3D模型快速生成虚拟场景和物品，应用于游戏、电影和虚拟试衣等行业，降低内容制作成本。
机器人导航与交互
提供丰富的3D环境模型，帮助机器人进行路径规划和场景理解，提升自动化水平。
Long-term Vision
数字孪生与文化遗产
通过高质量3D模型实现真实场景的数字复制，用于城市规划、文物保护和虚拟旅游，推动数字化转型。
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.