works on

From the 1 of 8 linked papers with an AI index.

activity
20242026
collaborators

8 papers

cs.RO2026

Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces

Xinggang Hu, Chenyangguang Zhang, Alexandros Delitzas +4

The paper introduces a method to build detailed, hierarchical functional 3D scene graphs for indoor environments, using open‑vocabulary visual grounding and temporal graph optimiza…

cs.CV2025

Street Gaussians without 3D Object Tracker

Ruida Zhang, Chengxi Li, Chenyangguang Zhang +5

Realistic scene reconstruction in driving scenarios poses significant challenges due to fast-moving objects. Most existing methods rely on labor-intensive manual labeling of object…

cs.CV2025

GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024

Xingyu Liu, Gu Wang, Chengxi Li +4

We present GFreeDet, an unseen object detection approach that leverages Gaussian splatting and vision Foundation models under model-free setting. Unlike existing methods that rely…

cs.CV2025

Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces

Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang +4

We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial…

cs.CV2025

GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation

Zinqin Huang, Gu Wang, Chenyangguang Zhang +3

Recent advances in RGBD-based category-level object pose estimation have been limited by their reliance on precise depth information, restricting their broader applicability. In re…

cs.CV2025

UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image

Xingyu Liu, Gu Wang, Ruida Zhang +3

Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimat…