KGpose: Keypoint-Graph Driven End-to-End Multi-Object 6D Pose Estimation via Point-Wise Pose Voting

• URL: http://arxiv.org/abs/2407.08909v1
• Date: Fri, 12 Jul 2024 01:06:00 GMT
• Title: KGpose: Keypoint-Graph Driven End-to-End Multi-Object 6D Pose Estimation via Point-Wise Pose Voting
• Authors: Andrew Jeong,
• Abstract summary: KGpose is an end-to-end framework for 6D pose estimation of multiple objects. Our approach combines keypoint-based method with learnable pose regression through keypoint-graph'
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This letter presents KGpose, a novel end-to-end framework for 6D pose estimation of multiple objects. Our approach combines keypoint-based method with learnable pose regression through `keypoint-graph', which is a graph representation of the keypoints. KGpose first estimates 3D keypoints for each object using an attentional multi-modal feature fusion of RGB and point cloud features. These keypoints are estimated from each point of point cloud and converted into a graph representation. The network directly regresses 6D pose parameters for each point through a sequence of keypoint-graph embedding and local graph embedding which are designed with graph convolutions, followed by rotation and translation heads. The final pose for each object is selected from the candidates of point-wise predictions. The method achieves competitive results on the benchmark dataset, demonstrating the effectiveness of our model. KGpose enables multi-object pose estimation without requiring an extra localization step, offering a unified and efficient solution for understanding geometric contexts in complex scenes for robotic applications.

Related papers

• CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network [66.24726878647543]
  Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet challenging task. Recent studies have shown the great potential of dense correspondence-based solutions. We propose a novel pose estimation algorithm named CheckerPose, which improves on three main aspects.
  arXiv  Detail & Related papers  (2023-03-29T17:30:53Z)
• Neural Correspondence Field for Object Pose Estimation [67.96767010122633]
  We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
  arXiv  Detail & Related papers  (2022-07-30T01:48:23Z)
• AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints [16.5436159805682]
  We propose a self-supervised method that learns to disentangle object structure from the appearance. Both the keypoint location and their pairwise edge weights are learned, given only a collection of images depicting the same object class. The resulting graph is interpretable, for example, AutoLink recovers the human skeleton topology when applied to images showing people.
  arXiv  Detail & Related papers  (2022-05-21T16:32:34Z)
• Coupled Iterative Refinement for 6D Multi-Object Pose Estimation [64.7198752089041]
  Given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy.
  arXiv  Detail & Related papers  (2022-04-26T18:00:08Z)
• A Dynamic Keypoints Selection Network for 6DoF Pose Estimation [0.0]
  6 DoF poses estimation problem aims to estimate the rotation and translation parameters between two coordinates. We present a novel deep neural network based on dynamic keypoints selection designed for 6DoF pose estimation from a single RGBD image.
  arXiv  Detail & Related papers  (2021-10-24T09:58:56Z)
• 6D Object Pose Estimation using Keypoints and Part Affinity Fields [24.126513851779936]
  The task of 6D object pose estimation from RGB images is an important requirement for autonomous service robots to be able to interact with the real world. We present a two-step pipeline for estimating the 6 DoF translation and orientation of known objects.
  arXiv  Detail & Related papers  (2021-07-05T14:41:19Z)
• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression [81.05772887221333]
  We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. We present a simple yet effective approach, named disentangled keypoint regression (DEKR) We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods.
  arXiv  Detail & Related papers  (2021-04-06T05:54:46Z)
• 3D Point-to-Keypoint Voting Network for 6D Pose Estimation [8.801404171357916]
  We propose a framework for 6D pose estimation from RGB-D data based on spatial structure characteristics of 3D keypoints. The proposed method is verified on two benchmark datasets, LINEMOD and OCCLUSION LINEMOD.
  arXiv  Detail & Related papers  (2020-12-22T11:43:15Z)
• Rapid Pose Label Generation through Sparse Representation of Unknown Objects [7.32172860877574]
  This work presents an approach for rapidly generating real-world, pose-annotated RGB-D data for unknown objects. We first source minimalistic labelings of an ordered set of arbitrarily chosen keypoints over a set of RGB-D videos. By solving an optimization problem, we combine these labels under a world frame to recover a sparse, keypoint-based representation of the object.
  arXiv  Detail & Related papers  (2020-11-07T15:14:03Z)
• Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement [54.29252286561449]
  We propose a two-stage graph-based and model-agnostic framework, called Graph-PCNN. In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled. In the second stage, for each guided point, different visual feature is extracted by the localization. The relationship between guided points is explored by the graph pose refinement module to get more accurate localization results.
  arXiv  Detail & Related papers  (2020-07-21T04:59:15Z)
• Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation [85.96410825961966]
  We argue that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries. To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions. We apply this proposed framework, called Point-Set Anchors, to object detection, instance segmentation, and human pose estimation.
  arXiv  Detail & Related papers  (2020-07-06T15:59:56Z)

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.