CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. CO3Dv2 enables research in multi-view 3D reconstruction, novel view synthesis, and geometry-aware representation learning. Each of the thousands of sequences in CO3Dv2 captures a common object (from categories like cars, chairs, or plants) from multiple real-world viewpoints. The dataset includes RGB images, depth maps, masks, and camera poses for each frame, along with pre-defined training, validation, and testing splits for both few-view and many-view reconstruction tasks.
Features
- Large-scale real-world dataset for 3D object category reconstruction
- 2× more sequences and 4× more frames than CO3Dv1
- High-quality images and segmentation masks with improved stability
- Organized into categories with detailed depth, mask, and point cloud data
- Ready-to-use few-view and many-view reconstruction subsets
- Supports CO3D Challenge on EvalAI for standardized evaluation