Unseen Object Instance Segmentation
7 papers with code • 2 benchmarks • 2 datasets
Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
Most implemented papers
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.
Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
Segmentation of unseen industrial parts is essential for autonomous industrial systems.
Unseen Object Instance Segmentation for Robotic Environments
We also show that our method can segment unseen objects for robot grasping.
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data.
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment.
Mean Shift Mask Transformer for Unseen Object Instance Segmentation
To illustrate the effectiveness of our method, we apply MSMFormer to unseen object instance segmentation.
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way.