4 citations · 5 across the 4 of their papers we have counts for
5 papers
Scene-Q: Confidence-Aware Coarse-to-Fine Querying of 3D Scenes with Selective VLM Reasoning
Juno Kim, Yesol Park, Hye-Jung Yoon +1
Indoor mobile robots require open-vocabulary scene understanding that grounds natural-language queries in a consistent 3D map. Many existing systems ultimately rely on cosine-simil…
CDIS: Cross-Dimensional Class-Agnostic 3D Instance Segmentation via 2D Mask Tracking and 3D-2D Projection Merging
Juno Kim, Hye-Jung Yoon, Yesol Park +1
Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulatio…
Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking
Hye-Jung Yoon, Juno Kim, Yesol Park +2
Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments. To overcome these…
DA-Fusion: Deformable Attention-Based RGB-D Fusion Transformer for Unseen Object Instance Segmentation
Yesol Park, Hye-Jung Yoon, Juno Kim +1
In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking…
OV-MAP: Open-Vocabulary Zero-Shot 3D Instance Segmentation Map for Robots
Juno Kim, Yesol Park, Hye-Jung Yoon +1
We introduce OV-MAP, a novel approach to open-world 3D mapping for mobile robots by integrating open-features into 3D maps to enhance object recognition capabilities. A significant…