6 citations · 8 across the 10 of their papers we have counts for
10 papers
VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video
Yu Liu, Baoxiong Jia, Ruijie Lu +5
Building digital twins of articulated objects from monocular video presents an essential challenge in computer vision, which requires simultaneous reconstruction of object geometry…
MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans
Huangyue Yu, Baoxiong Jia, Yixin Chen +9
Embodied AI (EAI) research requires high-quality, diverse 3D scenes to effectively support skill acquisition, sim-to-real transfer, and generalization. Achieving these quality stan…
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding
Yan Wang, Baoxiong Jia, Ziyu Zhu +1
Open-vocabulary 3D scene understanding is pivotal for enhancing physical intelligence, as it enables embodied agents to interpret and interact dynamically within real-world environ…
Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis
Jiangyong Huang, Baoxiong Jia, Yan Wang +5
Existing 3D vision-language (3D-VL) benchmarks fall short in evaluating 3D-VL models, creating a "mist" that obscures rigorous insights into model capabilities and 3D-VL tasks. Thi…
ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting
Yu Liu, Baoxiong Jia, Ruijie Lu +3
Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the ac…
SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields
Yu Liu, Baoxiong Jia, Yixin Chen +1
The ability to distill object-centric abstractions from intricate visual scenes underpins human-level generalization. Despite the significant progress in object-centric learning me…