Search Results for author:
Found 9 papers, 3 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.
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.
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.
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.
