原文:Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation 代码地址:hughw19.github.io/NOCS_ 摘要 本文的目的是估计RGB-D图像中未见过的对象实例的6D姿态和尺寸。与“实例级”6D姿态估计任务相反,我们的问题假设在训练或测试期间没有可用的精确对象CAD模型。为了处理给定类别中...
3、Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation(CVPR2019) 论文链接:https://arxiv.org/abs/1901.02970 代码链接:https://github.com/hughw19/NOCS_CVPR2019 本文的目标是估计RGB-D图像中从未见过的物体实例的6D位姿和尺寸。与“实例级”6D位姿估计任务相反,作者假设...
(但是作者没有说真实场景中的NOCS图的ground truth是怎么得到的) 模型 作者的网络结构是基于Mask R-CNN框架构建的,增加了预测NOCS图的分支。RGB图和深度图作为输入,CNN通过RGB图预测物体的类别标签、mask和NOCS图,之后将NOCS图与深度图进行拟合得到物体的6D位姿和大小(作者在CNN中没有使用深度图,因为作者使用COCO数...
3、Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation(CVPR2019) 论文链接:https://arxiv.org/abs/1901.02970 代码链接: https://github.com/hughw19/NOCS_CVPR2019 本文的目标是估计RGB-D图像中从未见过的物体实例的6D位姿和尺寸。与...
3、Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation(CVPR2019) 论文链接:https://arxiv.org/abs/1901.02970 代码链接:https://github.com/hughw19/NOCS_CVPR2019 本文的目标是估计RGB-D图像中从未见过的物体实例的6D位姿和尺寸。与“实例级”6D位姿估计任务相反,作者假设...
主页| https://ait.ethz.ch/projects/2020/neural-object-fitting/ 备注| ECCV 2020 [5]. Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation 6D物体姿态和尺寸估计 作者| Meng Tian, Marcelo H Ang Jr, Gim Hee Lee 单位| 新加坡国立大学 ...
[5]. Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation 6D物体姿态和尺寸估计 作者| Meng Tian, Marcelo H Ang Jr, Gim Hee Lee 单位| 新加坡国立大学 论文| https:///abs/2007.08454 代码| https://github.com/mentian/object-deformnet ...
标题:6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting 作者:Yufeng Jin, Vignesh Prasad, Snehal Jauhri, Mathias Franzius, Georgia Chalvatzaki 机构:Computer Science Department, Technische Universitat Darmstadt, Germany、Honda Research Institute Europe GmbH, Offenbach, Germany、Hessian.AI...
[1] NeRF-Feat: 6D Object Pose Estimation using Feature Rendering [2] Nerf-pose: A first-...
We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent...