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Found 4 papers, 3 papers with code
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications.
Unknown Object Segmentation from Stereo Images
This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map.
Multi-path Learning for Object Pose Estimation Across Domains
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together.
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization.
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6D Pose Estimation using RGBD
on T-LESS
