arXiv:2607. 17778v1 Announce Type: cross Abstract: Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulation and navigation.
By Juno Kim, Hye-Jung Yoon, Yesol Park, Byoung-Tak Zhang
arXiv:2506. 11585v2 Announce Type: replace-cross Abstract: 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.
By Juno Kim, Yesol Park, Hye-Jung Yoon, Byoung-Tak Zhang
arXiv:2606. 08014v1 Announce Type: cross Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications.
By Liang Xu, Fangjing Wang, Jinyu Yang, Feng Zheng
Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding.
Recent advancements in 3D Gaussian Splatting (3DGS) have enabled language-guided scene understanding. However, existing Referring 3D Gaussian Splatting (R3DGS) methods are fundamentally restricted to single-target queries.
arXiv:2607. 17754v1 Announce Type: cross Abstract: In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments.
By Yesol Park, Hye-Jung Yoon, Juno Kim, Byoung-Tak Zhang