Search Results for author:
Found 8 papers, 2 papers with code
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.
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.
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.
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition
RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios.
