Breadcrumbs Deep_Object_Pose /data_generation / readme.mdTop File metadata and controls Preview Code Blame 197 lines (137 loc) · 10.4 KB Raw Synthetic Data Generation This directory contains code for data generation (both images and associated JSON files) for training DOPE. We provide two va...
Deep Object Pose Estimation This is the official repository for NVIDIA's Deep Object Pose Estimation, which performs detection and 6-DoF pose estimation ofknown objectsfrom an RGB camera. For full details, see ourCoRL 2018 paperandvideo.
英伟达的机器人研究人员开发了一种基于深度学习的新系统,该系统允许机器人在其环境中感知家居物体,以获取物体并与之互动。通过这种技术,机器人能够对已知的家用物体进行简单的拾取操作,比如把一个物体交给一个人,或者从一个人的手中抓住一个物体。 这项研究基于英伟达研究人员之前的研究成果,允许机器人通过标准的RGB摄...
For a comprehensive understanding of the DOPE architecture and data generation pipeline, refer toDeep Object Pose Estimation for Semantic Robotic Grasping of Household Objects. Dataset NVIDIA providespretrained DOPE modelstrained on theNVIDIA Household Objects for Pose Estimation(HOPE) dataset. It...
A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation. Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper; we present a new vision-based robotic grasping system; whichcan not only re...
Fast Uncertainty Quantification for Deep Object Pose Estimationdoi:10.1109/ICRA48506.2021.9561483Guanya ShiYifeng ZhuJonathan TremblayStan BirchfieldFabio RamosAnimashree AnandkumarYuke ZhuIEEEInternational Conference on Robotics and Automation
In bin-picking scenarios, multiple instances of an object of interest are stacked in a pile randomly, and hence, the instances are inherently subjected to the challenges: severe occlusion, clutter, and similar-looking distractors. Most existing methods are, however, for single isolated object insta...
pose is to rely on a Deep Network to initially predict the 2D projections of some chosen 3D points and then compute the 3D pose of the object using a PnP method [9]. Such an approach has been shown to be more accurate than the approach of directly predicting the pose used in [5,6,...
5. Conclusion In this paper, we propose the DFTr network, a novel 6D object pose estimator with a powerful deep fusion trans- former block for cross-modalities feature aggregation. We further introduce a new efficient weighted vector-wise vot- ing algorithm...
In particular, we consider the problem of 3D object representation, and focus on different instances of the ShapeNet dataset. We propose a model that factorizes object shape, pose and category, while still learning a representation for each factor using a deep neural network. We show that ...