3 citations · 4 across the 15 of their papers we have counts for
17 papers
Fetch My Beer: Synthetic-to-real Hierarchical Policy for Smooth Pick-and-place
Yingyue Li, Chenyangguang Zhang, Ruida Zhang +3
Many real-world robotic applications require dynamically sensitive manipulation, where success depends not only on reaching a target state but on maintaining stable object dynamics…
3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints
Ziqin Huang, Yingyue Li, Chenyangguang Zhang +6
Intermediate representations are key to bridging the modality gap between generalizable manipulation policies and large-scale pretrained vision-language models (VLMs). Among these,…
Functional-SLAM: Interaction-Aware Mapping with Online Functional Scene Graphs
Xinggang Hu, Chenyangguang Zhang, Zihan Zhu +3
Existing SLAM systems lack modeling of the functional relations required for fine-grained robotic interaction. Functional 3D scene graphs can represent relations between objects an…
Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces
Xinggang Hu, Chenyangguang Zhang, Alexandros Delitzas +4
Functional 3D scene graphs offer a versatile and flexible representation for 3D scene understanding and robotic manipulation, defined by object nodes, interactive elements, and fun…
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…
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…
FA-BARF: Frequency Adapted Bundle-Adjusting Neural Radiance Fields
Rui Qian, Chenyangguang Zhang, Yan Di +5
Neural Radiance Fields (NeRF) have exhibited highly effective performance for photorealistic novel view synthesis recently. However, the key limitation it meets is the reliance on…
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…
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…
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…
LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation
Ruida Zhang, Ziqin Huang, Gu Wang +5
While RGBD-based methods for category-level object pose estimation hold promise, their reliance on depth data limits their applicability in diverse scenarios. In response, recent e…
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications
Xingyu Liu, Chenyangguang Zhang, Gu Wang +2
In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to…
KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation
Ruida Zhang, Chenyangguang Zhang, Yan Di +4
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometri…
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction
Bowen Fu, Gu Wang, Chenyangguang Zhang +6
Reconstructing hand-held objects from a single RGB image is a challenging task in computer vision. In contrast to prior works that utilize deterministic modeling paradigms, we empl…
SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
Yamei Chen, Yan Di, Guangyao Zhai +6
Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. Exist…
ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation
Yan Di, Chenyangguang Zhang, Chaowei Wang +6
In this paper, we present ShapeMatcher, a unified self-supervised learning framework for joint shape canonicalization, segmentation, retrieval and deformation. Given a partially-ob…
MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision
Chenyangguang Zhang, Guanlong Jiao, Yan Di +7
Previous works concerning single-view hand-held object reconstruction typically rely on supervision from 3D ground-truth models, which are hard to collect in real world. In contras…