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Found 30 papers, 13 papers with code
Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation
In this study, we compare image-to-image translation networks based on GANs and diffusion models for the downstream task of 6D object pose estimation.
STAR: Shape-focused Texture Agnostic Representations for Improved Object Detection and 6D Pose Estimation
To achieve a focus on learning shape features, the textures are randomized during the rendering of the training data.
Real-time 6-DoF Pose Estimation by an Event-based Camera using Active LED Markers
Experimental results in static and dynamic scenarios are presented to demonstrate the performance of the proposed approach in terms of computational speed and absolute accuracy, using the OptiTrack system as the basis for measurement.
ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers
The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects.
TrackAgent: 6D Object Tracking via Reinforcement Learning
Tracking an object's 6D pose, while either the object itself or the observing camera is moving, is important for many robotics and augmented reality applications.
Challenges for Monocular 6D Object Pose Estimation in Robotics
Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding.
Self-supervised Vision Transformers for 3D Pose Estimation of Novel Objects
This work evaluates and demonstrates the differences between self-supervised CNNs and Vision Transformers for deep template matching.
Open Challenges for Monocular Single-shot 6D Object Pose Estimation
Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases.
Grasping the Inconspicuous
Towards this, we study deep learning 6D pose estimation from RGB images only for transparent object grasping.
COPE: End-to-end trainable Constant Runtime Object Pose Estimation
We present an approach that learns an intermediate geometric representation of multiple objects to directly regress 6D poses of all instances in a test image.
SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement
Observational noise, inaccurate segmentation and ambiguity due to symmetry and occlusion lead to inaccurate object pose estimates.
Event-Based high-speed low-latency fiducial marker tracking
Motion and dynamic environments, especially under challenging lighting conditions, are still an open issue for robust robotic applications.
UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments
Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem.
ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning
Point cloud registration is a common step in many 3D computer vision tasks such as object pose estimation, where a 3D model is aligned to an observation.
Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme
While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem.
PyraPose: Feature Pyramids for Fast and Accurate Object Pose Estimation under Domain Shift
We also perform grasping experiments in the real world to demonstrate the advantage of using synthetic data to generalize to novel environments.
Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images
Recent methods for 6D pose estimation of objects assume either textured 3D models or real images that cover the entire range of target poses.
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions.
Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber
The proposed system is based on multiple sensors including 3D and 2D lidar, two RGB-D cameras and a stereo camera.
Robotics
DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-based Robotic Grasping
This article presents a method for grasping novel objects by learning from experience.
Robotics
Addressing the Sim2Real Gap in Robotic 3D Object Classification
In this work, we examine this gap in a robotic context by specifically addressing the problem of classification when transferring from artificial CAD models to real reconstructed objects.
VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement
The generality of the approach is shown by using three state-of-the-art pose estimators and three baseline refiners.
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries.
Ranked #1 on
6D Pose Estimation using RGB
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EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets
This paper presents the EasyLabel tool for easily acquiring high quality ground truth annotation of objects at the pixel-level in densely cluttered scenes.
Recurrent Convolutional Fusion for RGB-D Object Recognition
Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision.
High Dynamic Range SLAM with Map-Aware Exposure Time Control
The research in dense online 3D mapping is mostly focused on the geometrical accuracy and spatial extent of the reconstructions.
Recognizing Objects In-the-wild: Where Do We Stand?
The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments.
Using Dimension Reduction to Improve the Classification of High-dimensional Data
In this work we show that the classification performance of high-dimensional structural MRI data with only a small set of training examples is improved by the usage of dimension reduction methods.
Object Modelling with a Handheld RGB-D Camera
This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor.
Find my mug: Efficient object search with a mobile robot using semantic segmentation
In this paper, we propose an efficient semantic segmentation framework for indoor scenes, tailored to the application on a mobile robot.
