Geo6D: Geometric Constraints Learning for 6D Pose Estimation

• URL: http://arxiv.org/abs/2210.10959v6
• Date: Tue, 22 Aug 2023 01:31:55 GMT
• Title: Geo6D: Geometric Constraints Learning for 6D Pose Estimation
• Authors: Jianqiu Chen, Mingshan Sun, Ye Zheng, Tianpeng Bao, Zhenyu He, Donghai Li, Guoqiang Jin, Rui Zhao, Liwei Wu, Xiaoke Jiang
• Abstract summary: We propose a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. We show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets.
• Score: 21.080439293774464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data.

Related papers

• End-to-End Probabilistic Geometry-Guided Regression for 6DoF Object Pose Estimation [5.21401636701889]
  State-of-the-art 6D object pose estimators directly predict an object pose given an object observation. We reformulate the state-of-the-art algorithm GDRNPP and introduce EPRO-GDR. Our solution shows that predicting a pose distribution instead of a single pose can improve state-of-the-art single-view pose estimation.
  arXiv  Detail & Related papers  (2024-09-18T09:11:31Z)
• 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)
• 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)
• FS6D: Few-Shot 6D Pose Estimation of Novel Objects [116.34922994123973]
  6D object pose estimation networks are limited in their capability to scale to large numbers of object instances. In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training.
  arXiv  Detail & Related papers  (2022-03-28T10:31:29Z)
• Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation [8.464912344558481]
  We propose novel parameterizations for the output of the neural network for single shot 6D object pose estimation. Our learning-based approach achieves state-of-the-art performance on two public benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-15T15:29:53Z)
• 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)
• Self6D: Self-Supervised Monocular 6D Object Pose Estimation [114.18496727590481]
  We propose the idea of monocular 6D pose estimation by means of self-supervised learning. We leverage recent advances in neural rendering to further self-supervise the model on unannotated real RGB-D data.
  arXiv  Detail & Related papers  (2020-04-14T13:16:36Z)
• Object 6D Pose Estimation with Non-local Attention [29.929911622127502]
  We propose a network that integrate 6D object pose parameter estimation into the object detection framework. The proposed method reaches the state-of-the-art performance on the YCB-video and the Linemod datasets.
  arXiv  Detail & Related papers  (2020-02-20T14:23:32Z)

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.