ZeroBP: Learning Position-Aware Correspondence for Zero-shot 6D Pose Estimation in Bin-Picking
- URL: http://arxiv.org/abs/2502.01004v1
- Date: Mon, 03 Feb 2025 02:57:50 GMT
- Title: ZeroBP: Learning Position-Aware Correspondence for Zero-shot 6D Pose Estimation in Bin-Picking
- Authors: Jianqiu Chen, Zikun Zhou, Xin Li, Ye Zheng, Tianpeng Bao, Zhenyu He,
- Abstract summary: Bin-picking is a practical and challenging robotic manipulation task, where accurate 6D pose estimation is required.
Existing solutions are typically learning and object-specific estimation methods.
We propose a Zero-shot pose estimation framework specifically for the bin-picking task.
We achieve an improvement of 9.1% in average recall estimation methods, achieving an 9.1% in correct poses.
- Score: 13.844868269077724
- License:
- Abstract: Bin-picking is a practical and challenging robotic manipulation task, where accurate 6D pose estimation plays a pivotal role. The workpieces in bin-picking are typically textureless and randomly stacked in a bin, which poses a significant challenge to 6D pose estimation. Existing solutions are typically learning-based methods, which require object-specific training. Their efficiency of practical deployment for novel workpieces is highly limited by data collection and model retraining. Zero-shot 6D pose estimation is a potential approach to address the issue of deployment efficiency. Nevertheless, existing zero-shot 6D pose estimation methods are designed to leverage feature matching to establish point-to-point correspondences for pose estimation, which is less effective for workpieces with textureless appearances and ambiguous local regions. In this paper, we propose ZeroBP, a zero-shot pose estimation framework designed specifically for the bin-picking task. ZeroBP learns Position-Aware Correspondence (PAC) between the scene instance and its CAD model, leveraging both local features and global positions to resolve the mismatch issue caused by ambiguous regions with similar shapes and appearances. Extensive experiments on the ROBI dataset demonstrate that ZeroBP outperforms state-of-the-art zero-shot pose estimation methods, achieving an improvement of 9.1% in average recall of correct poses.
Related papers
- Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation [9.637714330461037]
We propose a novel method of hard example synthesis that is model-agnostic.
We demonstrate an improvement in correct detection rate of up to 20% across several ROBI-dataset objects using state-of-the-art pose estimation models.
arXiv Detail & Related papers (2024-12-05T16:00:55Z) - Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot,
Generalizable Approach using RGB Images [60.0898989456276]
We present a new framework named Cas6D for few-shot 6DoF pose estimation that is generalizable and uses only RGB images.
To address the false positives of target object detection in the extreme few-shot setting, our framework utilizes a self-supervised pre-trained ViT to learn robust feature representations.
Experimental results on the LINEMOD and GenMOP datasets demonstrate that Cas6D outperforms state-of-the-art methods by 9.2% and 3.8% accuracy (Proj-5) under the 32-shot setting.
arXiv Detail & Related papers (2023-06-13T07:45:42Z) - ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes [19.993163470302097]
ZeroPose is a novel framework that performs pose estimation following a Discovery-Orientation-Registration (DOR) inference pipeline.
It generalizes to novel objects without requiring model retraining.
It achieves comparable performance with object-specific training methods and outperforms the state-of-the-art zero-shot method with 50x inference speed improvement.
arXiv Detail & Related papers (2023-05-29T07:54:04Z) - PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching [51.142988196855484]
We propose PoseMatcher, an accurate model free one-shot object pose estimator.
We create a new training pipeline for object to image matching based on a three-view system.
To enable PoseMatcher to attend to distinct input modalities, an image and a pointcloud, we introduce IO-Layer.
arXiv Detail & Related papers (2023-04-03T21:14:59Z) - Rigidity-Aware Detection for 6D Object Pose Estimation [60.88857851869196]
Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose.
We propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid.
Key to the success of our approach is a visibility map, which we propose to build using a minimum barrier distance between every pixel in the bounding box and the box boundary.
arXiv Detail & Related papers (2023-03-22T09:02:54Z) - Unseen Object 6D Pose Estimation: A Benchmark and Baselines [62.8809734237213]
We propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing.
We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set.
By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently.
arXiv Detail & Related papers (2022-06-23T16:29:53Z) - Zero-Shot Category-Level Object Pose Estimation [24.822189326540105]
We tackle the problem of estimating the pose of novel object categories in a zero-shot manner.
This extends much of the existing literature by removing the need for pose-labelled datasets or category-specific CAD models.
Our method provides a six-fold improvement in average rotation accuracy at 30 degrees.
arXiv Detail & Related papers (2022-04-07T17:58:39Z) - CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild [45.93626858034774]
Category-level PPF voting method to achieve accurate, robust and generalizable 9D pose estimation in the wild.
A novel coarse-to-fine voting algorithm is proposed to eliminate noisy point pair samples and generate final predictions from the population.
Our method is on par with current state of the arts with real-world training data.
arXiv Detail & Related papers (2022-03-07T01:36:22Z) - Spatial Attention Improves Iterative 6D Object Pose Estimation [52.365075652976735]
We propose a new method for 6D pose estimation refinement from RGB images.
Our main insight is that after the initial pose estimate, it is important to pay attention to distinct spatial features of the object.
We experimentally show that this approach learns to attend to salient spatial features and learns to ignore occluded parts of the object, leading to better pose estimation across datasets.
arXiv Detail & Related papers (2021-01-05T17:18:52Z) - CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular
Images With Self-Supervised Learning [74.53664270194643]
Modern monocular 6D pose estimation methods can only cope with a handful of object instances.
We propose a novel method for class-level monocular 6D pose estimation, coupled with metric shape retrieval.
We experimentally demonstrate that we can retrieve precise 6D poses and metric shapes from a single RGB image.
arXiv Detail & Related papers (2020-03-12T15:28:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.