OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models (2301.07673v1)

Abstract: We propose a new method for object pose estimation without CAD models. The previous feature-matching-based method OnePose has shown promising results under a one-shot setting which eliminates the need for CAD models or object-specific training. However, OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. Built upon the detector-free feature matching method LoFTR, we devise a new keypoint-free SfM method to reconstruct a semi-dense point-cloud model for the object. Given a query image for object pose estimation, a 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model without first detecting keypoints in the image. Experiments show that the proposed pipeline outperforms existing one-shot CAD-model-free methods by a large margin and is comparable to CAD-model-based methods on LINEMOD even for low-textured objects. We also collect a new dataset composed of 80 sequences of 40 low-textured objects to facilitate future research on one-shot object pose estimation. The supplementary material, code and dataset are available on the project page: https://zju3dv.github.io/onepose_plus_plus/.

• The paper introduces a keypoint-free structure-from-motion approach that bypasses traditional CAD models for pose estimation.
• It employs a direct 2D-3D matching network to achieve accurate object pose results, particularly for low-textured objects.
• It provides a new benchmark dataset and demonstrates superior performance compared to existing one-shot, CAD-free methods.

OnePose++: Advancements in Keypoint-Free One-Shot Object Pose Estimation

The paper "OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models" introduces a novel methodology for addressing object pose estimation challenges, particularly those associated with low-textured objects. This work builds on the predecessor OnePose, leveraging a feature-matching-based approach for one-shot object pose estimation but without relying on CAD models or object-specific training. The researchers emphasize removing the dependency on keypoints, which often falters when dealing with objects that lack distinct textures, by proposing a keypoint-free pipeline.

Key Contributions

The core contributions of this paper are multifaceted:

1. Keypoint-Free Structure from Motion (SfM): The authors propose a method that uses a detector-free feature matching approach, inspired by the LoFTR method, to reconstruct a semi-dense point-cloud model of the object. Unlike conventional methods that rely on keypoint detection, this approach enables effective handling of low-textured objects by constructing consistent feature tracks across multiple views.
2. Direct 2D-3D Matching Network: The proposed framework employs a novel stage where the 2D-3D correspondences necessary for pose estimation are established directly, bypassing the traditional keypoint detection step. This aspect is crucial for improving pose estimation accuracy in scenarios with repetitive patterns or sparse texture.
3. New Benchmark Dataset: To facilitate further research, the authors introduce a dataset comprising 80 sequences of 40 low-textured objects. This dataset is intended to challenge and refine future methods in one-shot object pose estimation.

Experimental Evaluation

The paper reports extensive experimentation on several datasets, including the OnePose dataset, LINEMOD, and the newly created OnePose-LowTexture dataset. Key findings include:

• Superior Performance: The proposed method significantly outperforms existing one-shot, CAD-model-free methods, demonstrating comparable results to CAD-model-based techniques such as those evaluated on LINEMOD.
• Effective for Low-Textured Objects: The method displayed robust performance improvement over the baseline techniques, especially on low-textured object sequences.
• Efficiency: The approach efficiently refines point clouds even with limited texture features, highlighting the proposed sparse-to-dense 2D-3D matching strategy’s capability.

Implications and Future Directions

The OnePose++ framework holds significant implications for real-world applications, particularly in augmented reality (AR), robotics, and autonomous systems, where real-time, accurate object pose estimation is vital. This research underscores a shift away from dependency on detailed CAD models, providing an adaptable alternative tailored for generalizable, real-time deployments.

Future developments in this area might focus on enhancing scalability and robustness in broader contexts with varying environmental conditions. The integration with emerging sensor technologies or the incorporation of models with self-learning capabilities could further improve accuracy and adaptability. Additionally, exploring fusion approaches that consider multi-modal data could offer new insights into further handling low-textured environments.

In summary, the OnePose++ paper presents a significant advancement in object pose estimation by addressing the limitations of texture dependency through a keypoint-free approach. Its implications for both theoretical exploration and practical application suggest a promising trajectory for future AI and computer vision research in dynamic and complex settings.