StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation
About the Dataset
393K high-resolution stereo RGB frames from a hand-held stereo RGB camera
18 objects including 8 symmetric objects, 7 transparent objects, and 8 reflective objects. Each object is released with 3D mesh models, scanned with a 0.04mm-precision scanner
Largest real-data object pose estimation dataset in history
1.5M 6D object pose annotations
Bounding boxes, instance masks of each object provided
Proposed multi-view annotation method using object keypoints achieves the highest annotation precision among all published object pose estimation datasets
Objects and Data Samples
Video
stereobj1m_video.mp4Abstract
We present a large-scale stereo RGB image object pose estimation dataset named the StereOBJ-1M dataset. The dataset is designed to address challenging cases such as object transparency, translucency, and specular reflection, in addition to the common challenges of occlusion, symmetry, and variations in illumination and environments. In order to collect data of sufficient scale for modern deep learning models, we propose a novel method for efficiently annotating pose data in a multi-view fashion that allows data capturing in complex and flexible environments. Fully annotated with 6D object poses, our dataset contains over 393K frames and over 1.5M annotations of 18 objects recorded in 182 scenes constructed in 11 different environments. The 18 objects include 8 symmetric objects, 7 transparent objects, and 8 reflective objects. We benchmark two state-of-the-art pose estimation frameworks on StereOBJ-1M as baselines for future work. We also propose a novel object-level pose optimization method for computing 6D pose from keypoint predictions in multiple images.
Citation
If you find our dataset useful in your research, please cite the following:
@inproceedings{liu2021stereobj-1m,
title = {StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation},
author = {Xingyu Liu and Shun Iwase and Kris M. Kitani},
booktitle = {The International Conference on Computer Vision (ICCV 2021)},
year = {2021},
}
Terms and Conditions
This dataset is released under the Creative Commons 4.0 License.