.png){kind=icon}
返回
Efficient 6DoF pose estimation for multi-instance objects from a single image
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2025.105882.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Estimating 6 degrees of freedom poses for multiple objects from a single image and making it practical in industry is difficult since several metrics, like accuracy, speed and complexity must be traded. This study adopts a fast bottom-up approach to estimate poses for multi-instance objects in an image simultaneously. We design a convolutional neural network with simple end-to-end training to output 4 feature maps: error mask, semantic mask, center vector map and 6D coordinate map (6DCM). Specifically, 6DCM is capable of providing the rear-side 3D object point clouds information that are originally invisible from the camera's viewpoint. This procedure enriches shape information about target objects which can be used to construct each instance's 2D-3D correspondences for pose parameter estimation. Experimental results show that our proposed bottom-up approach is fast and can process a single image containing 7 objects at 25 frames per second with competitive accuracy to other top-down methods.
Keyword:
6DoF
Object pose estimation
Bottom-up approach
Multi-instances objects
Deep learning neural network
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
SEMPose: A single end-to-end network for multi-object pose estimationSEMPose：用于多目标姿态估计的单个端到端网络.png)
Neurocomputing
NeurocomputingIF6.5
PREAI
分享收藏
Detecting Object Surface Keypoints From a Single RGB Image via Deep Learning Network for 6-DoF Pose EstimationIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity FieldsOpenPose: 使用部分关联字段的实时多人2D姿态估计OAAI
OAAI分享收藏MMFEIR: Multi-attention Mutual Feature Enhance and Instance Reconstruction for category-level 6D object pose estimationMMFEIR：多注意力互特征增强和实例重建用于类别级6D物体位姿估计
PREAI
分享收藏REG-Net: Improving 6DoF Object Pose Estimation With 2D Keypoint Long-Short-Range-Aware Registration
PREAI
分享收藏
查看更多内容