HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation

• URL: http://arxiv.org/abs/2004.00060v1
• Date: Tue, 31 Mar 2020 19:01:42 GMT
• Title: HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation
• Authors: Bardia Doosti, Shujon Naha, Majid Mirbagheri, David Crandall
• Abstract summary: We propose a lightweight model called HOPE-Net which jointly estimates hand and object pose in 2D and 3D in real-time. Our network uses a cascade of two adaptive graph convolutional neural networks, one to estimate 2D coordinates of the hand joints and object corners, followed by another to convert 2D coordinates to 3D.
• Score: 7.559220068352681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a hand and of a held object. In this paper, we propose a lightweight model called HOPE-Net which jointly estimates hand and object pose in 2D and 3D in real-time. Our network uses a cascade of two adaptive graph convolutional neural networks, one to estimate 2D coordinates of the hand joints and object corners, followed by another to convert 2D coordinates to 3D. Our experiments show that through end-to-end training of the full network, we achieve better accuracy for both the 2D and 3D coordinate estimation problems. The proposed 2D to 3D graph convolution-based model could be applied to other 3D landmark detection problems, where it is possible to first predict the 2D keypoints and then transform them to 3D.

Related papers

• Improving 3D Pose Estimation for Sign Language [38.20064386142944]
  This work addresses 3D human pose reconstruction in single images. We present a method that combines Forward Kinematics (FK) with neural networks to ensure a fast and valid prediction of 3D pose.
  arXiv  Detail & Related papers  (2023-08-18T13:05:10Z)
• Multi-View Representation is What You Need for Point-Cloud Pre-Training [22.55455166875263]
  This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks. We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks.
  arXiv  Detail & Related papers  (2023-06-05T03:14:54Z)
• 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)
• Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training [65.75399500494343]
  Masked Autoencoders (MAE) have shown promising performance in self-supervised learning for 2D and 3D computer vision. We propose Joint-MAE, a 2D-3D joint MAE framework for self-supervised 3D point cloud pre-training.
  arXiv  Detail & Related papers  (2023-02-27T17:56:18Z)
• SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space [14.81199315166042]
  We propose a method for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning. Based on the estimation results for the dataset Human3.6M, our approach outperforms many state-of-the-art methods both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-06-04T00:51:00Z)
• AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [15.244852122106634]
  We propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain. For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed.
  arXiv  Detail & Related papers  (2021-08-25T08:50:06Z)
• Weakly-supervised Cross-view 3D Human Pose Estimation [16.045255544594625]
  We propose a simple yet effective pipeline for weakly-supervised cross-view 3D human pose estimation. Our method can achieve state-of-the-art performance in a weakly-supervised manner. We evaluate our method on the standard benchmark dataset, Human3.6M.
  arXiv  Detail & Related papers  (2021-05-23T08:16:25Z)
• FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [78.00922683083776]
  It is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020.
  arXiv  Detail & Related papers  (2021-04-22T09:35:35Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)
• Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem [98.92148855291363]
  This paper proposes a deep CNN model which simultaneously solves for both 6-DoF absolute camera pose 2D--3D correspondences. Tests on both real and simulated data have shown that our method substantially outperforms existing approaches.
  arXiv  Detail & Related papers  (2020-03-15T04:17:30Z)

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.