Texpose: Neural texture learning for self-supervised 6d object pose estimation
In this paper, we introduce neural texture learning for 6D object pose estimation from
synthetic data and a few unlabelled real images. Our major contribution is a novel learning …
synthetic data and a few unlabelled real images. Our major contribution is a novel learning …
HouseCat6D-A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision. Building on
successful instance-level approaches research is shifting towards category-level pose …
successful instance-level approaches research is shifting towards category-level pose …
In-hand 3d object scanning from an rgb sequence
We propose a method for in-hand 3D scanning of an unknown object with a monocular
camera. Our method relies on a neural implicit surface representation that captures both the …
camera. Our method relies on a neural implicit surface representation that captures both the …
Self-supervised category-level 6d object pose estimation with optical flow consistency
Category-level 6D object pose estimation aims at determining the pose of an object of a
given category. Most current state-of-the-art methods require a significant amount of real …
given category. Most current state-of-the-art methods require a significant amount of real …
On the importance of accurate geometry data for dense 3D vision tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor
data. The respectively used principle of measuring distances provides advantages and …
data. The respectively used principle of measuring distances provides advantages and …
SP: Self-Supervised Polarimetric Pose Prediction
This paper proposes the first self-supervised 6D object pose prediction from multimodal
RGB+ polarimetric images. The novel training paradigm comprises (1) a physical model to …
RGB+ polarimetric images. The novel training paradigm comprises (1) a physical model to …
Robot Grasp Planning: A Learning from Demonstration-Based Approach
Robot grasping constitutes an essential capability in fulfilling the complexities of advanced
industrial operations. This field has been extensively investigated to address a range of …
industrial operations. This field has been extensively investigated to address a range of …
Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and high-
performing solutions to a given problem. Originally developed for evolutionary robotics, most …
performing solutions to a given problem. Originally developed for evolutionary robotics, most …
Gs-pose: Category-level object pose estimation via geometric and semantic correspondence
Category-level pose estimation is a challenging task with many potential applications in
computer vision and robotics. Recently, deep-learning-based approaches have made great …
computer vision and robotics. Recently, deep-learning-based approaches have made great …
Efficient reinforcement learning with least-squares soft Bellman residual for robotic grasping
Y Lan, J Ren, T Tang, X Xu, Y Shi, Z Tang - Robotics and Autonomous …, 2023 - Elsevier
Grasping control of intelligent robots has to deal with the difficulties of model uncertainties
and nonlinearities. In this paper, we propose the Kernel-based Least-Squares Soft Bellman …
and nonlinearities. In this paper, we propose the Kernel-based Least-Squares Soft Bellman …