OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
OnePose++ is an innovative project designed to simplify the process of estimating the pose of objects from just one image, without the need for CAD models. This project was introduced by a team of researchers including Xingyi He, Jiaming Sun, Yu'ang Wang, Di Huang, Hujun Bao, and Xiaowei Zhou and presented at NeurIPS 2022.
Project Highlights
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Keypoint-Free Estimation: Traditional methods rely heavily on identifying keypoints or using CAD models to estimate the pose of objects. OnePose++ bypasses these needs, offering a more streamlined solution.
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One-Shot Capability: It effectively determines the pose from a single image, making it highly efficient for real-world applications where acquiring multiple images can be challenging.
Current Progress and Features
The project is a work in progress, with several exciting features already available and more under development:
- Complete with training, inference, and demo code that is ready for use.
- A functioning pipeline to reproduce evaluation results on both the OnePose dataset and the proposed OnePose_LowTexture dataset.
- Works are ongoing to enhance the system's capability to employ multiple GPUs for parallelized reconstruction and evaluation of various objects.
- The team is preparing to release "OnePose Cap," a data capture app for iOS users.
Installation and Dependencies
To use OnePose++, users will need to set up the development environment by creating and activating a Conda environment. Several key external projects such as LoFTR and DeepLM are utilized in this project, contributing to its robust functionality. Installation instructions and license agreements from these projects will need to be followed.
Users also need to download specific pretrained models necessary for 2D-3D matching which are essential for the functioning of the system.
Visualization Tools
OnePose++ offers an optional web-based 3D visualization tool, Wis3D, that allows users to view and interact with feature matches and point clouds.
Running the Demo
After installation, the demo can be run with custom data to see the project in action. Detailed steps are available in the project documentation to guide users through this process.
Training and Evaluation
OnePose++ provides a structured path for training and evaluation:
- Datasets such as OnePose, OnePose_LowTexture, and LINEMOD can be downloaded and set up with specific directory structures.
- Reconstruction processes for obtaining object point clouds and 2D-3D correspondences are detailed.
- Inference scripts for evaluating datasets, including OnePose and LINEMOD, are available.
- The system supports training with ground-truth annotations and offers flexibility in GPU usage to accommodate various hardware configurations.
Future Prospects
While some parts of the project remain under development, such as the multiple GPU capabilities for reconstruction, the roadmap indicates ongoing enhancements that will make the system more versatile and powerful.
Recognition
If OnePose++ proves beneficial in your research, the team encourages users to cite their work, thus acknowledging the effort and expertise that went into developing this groundbreaking system.
Acknowledgments
The project acknowledges the contribution of code from other works such as hloc and LoFTR, reflecting a collaborative ethos within the research community.
For more detailed information, users can visit the project's homepage or refer to the research paper.