In light of the shortcomings of existing Transformer-based pose estimate methods when handling localized features, this work presents MAQT, an enhanced end-to-end method aimed at precise multi-human body pose estimation. To improve the localization of keypoints that are sensitive to scale changes,...
1、整体网络结构 1.1.PoseDecoder ,并经过MLP来得到topk个初始init kpt,然后将其编码成D维作为上图中的 ,而init kpt作为DeformableModule的初始参考点,然后在每个参考点附近预测一个sampling offset并取出对应位置的特征向量。最终经过MLP预测出Pose和Score。 论文中采用了3层Decoder,并也用了动态迭代Pose的思想: ...
Abstract Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a result, they are difficult to scale to a large number of objects and cannot be directly applied to unseen object...
End-to-End Trainable Multi-Instance Pose Estimation with Transformers End-to-End:带有变压器的端到端可训练多实例姿势估计 链接PFD 我们提出了一种新的端到端可培训方法,用于结合卷积的多实例姿态估计带变压器的神经网络。我们投射多实例姿势图像估计作为直接集预测问题。受到端到端可训练对象检测的最新工作的启发对...
Paper tables with annotated results for End-to-End Probabilistic Geometry-Guided Regression for 6DoF Object Pose Estimation
We propose a novel end-to-end trainable framework for the graph decomposition problem. The minimum cost multicut problem is first converted to an unconstrained binary cubic formulation where cycle consistency constraints are incorporated into the objective function. The new optimization problem can be ...
object pose. Yet, learning the entire set of unrestricted 2D-3D points from scratch fails to converge with existing approaches, since the deterministic pose is inherently non-differentiable. In this paper, we propose the EPro-PnP, a probabi...
技术标签:Pose estimation 目标检测文章阅读(一) 文章《End-to-end Object Detection with Transformer》 code: https://github.com/facebookresearch/detr Introduction 本篇文章是比较有影响力的DETR,开End-to-end object detection之先河。 之前的object detection的model,ex... 查看...
[ICCV 2023] The official PyTorch code for Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation - Atten4Vis/GroupPose
Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of correspondences for pose estimation. In this work, ...