Papers Unseen Object Instance Segmentation
“Unseen Object Instance Segmentation” 태그가 달린 논문 14편 · 필터 해제
rt-RISeg: Real-Time Model-Free Robot Interactive Segmentation for Active Instance-Level Object Understanding
Successful execution of dexterous robotic manipulation tasks in new environments, such as grasping, depends on the ability to proficiently segment unseen objects from the background and other objects. Previous works in u…
Unseen Object Instance SegmentationInteractive SegmentationObject SegmentationZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models
Service robots operating in unstructured environments must effectively recognize and segment unknown objects to enhance their functionality. Traditional supervised learningbased segmentation techniques require extensive …
Instance SegmentationObjectSegmentationSemantic Segmentation+1Adapting Segment Anything Model for Unseen Object Instance Segmentation
Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments. Previous approaches require full supervision on large-scale tabletop datasets for effective pretraining.…
DecoderInstance SegmentationSegmentationSemantic Segmentation+1RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features
In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other objects. Previous works perform unseen ob…
Instance SegmentationObjectSegmentationSemantic Segmentation+1High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement
Accurate and efficient segmentation of unknown objects in unstructured environments is essential for robotic manipulation. Unknown Object Instance Segmentation (UOIS), which aims to identify all objects in unknown catego…
Instance SegmentationObjectSegmentationSemantic Segmentation+1Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then …
Instance SegmentationMulti-Object TrackingObjectObject Tracking+6Mean Shift Mask Transformer for Unseen Object Instance Segmentation
Segmenting unseen objects from images is a critical perception skill that a robot needs to acquire. In robot manipulation, it can facilitate a robot to grasp and manipulate unseen objects. Mean shift clustering is a wide…
ClusteringImage SegmentationInstance SegmentationObject+4Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation
Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features of large-scale synthetic data and direc…
Instance SegmentationKnowledge DistillationSegmentationSemantic Segmentation+3Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limited to segmenting the only visible regio…
Amodal Instance SegmentationInstance SegmentationObjectSegmentation+2Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation…
ClusteringInstance SegmentationMetric LearningObject+3Unseen Object Instance Segmentation for Robotic Environments
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environme…
Instance SegmentationObjectSegmentationSemantic Segmentation+1Segmenting 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. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy o…
Instance SegmentationSegmentationSynthetic Data GenerationUnseen Object Instance SegmentationThe Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
In order to function in unstructured environments, robots need the ability to recognize unseen novel objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop env…
Instance SegmentationObjectSegmentationSemantic Segmentation+1Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment spe…
ClusteringDataset GenerationInstance SegmentationObject Tracking+2