15 citations · 72 across the 27 of their papers we have counts for
28 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…
FlowScene: Style-Consistent Indoor Scene Generation with Multimodal Graph Rectified Flow
Zhifei Yang, Guangyao Zhai, Keyang Lu +6
Scene generation has extensive industrial applications, demanding both high realism and precise control over geometry and appearance. Language-driven retrieval methods compose plau…
STARE-VLA: Progressive Stage-Aware Reinforcement for Fine-Tuning Vision-Language-Action Models
Feng Xu, Guangyao Zhai, Xin Kong +4
Recent advances in Vision-Language-Action (VLA) models, powered by large language models and reinforcement learning-based fine-tuning, have shown remarkable progress in robotic man…
Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors
Guangyao Zhai, Yue Zhou, Xinyan Deng +3
Few-shot anomaly detection streamlines and simplifies industrial safety inspection. However, limited samples make accurate differentiation between normal and abnormal features chal…
KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
Bingran Chen, Baorun Li, Jian Yang +2
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data…
Video Perception Models for 3D Scene Synthesis
Rui Huang, Guangyao Zhai, Zuria Bauer +5
Traditionally, 3D scene synthesis requires expert knowledge and significant manual effort. Automating this process could greatly benefit fields such as architectural design, roboti…
SG-Tailor: Inter-Object Commonsense Relationship Reasoning for Scene Graph Manipulation
Haoliang Shang, Hanyu Wu, Guangyao Zhai +4
Scene graphs capture complex relationships among objects, serving as strong priors for content generation and manipulation. Yet, reasonably manipulating scene graphs -- whether by…
Think Before Refusal : Triggering Safety Reflection in LLMs to Mitigate False Refusal Behavior
Shengyun Si, Xinpeng Wang, Guangyao Zhai +2
Recent advancements in large language models (LLMs) have demonstrated that fine-tuning and human alignment can render LLMs harmless. In practice, such "harmlessness" behavior is ma…
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…
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion
Guangyao Zhai, Evin Pınar Örnek, Dave Zhenyu Chen +5
We present EchoScene, an interactive and controllable generative model that generates 3D indoor scenes on scene graphs. EchoScene leverages a dual-branch diffusion model that dynam…
GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering
Yanyan Li, Chenyu Lyu, Yan Di +3
During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved, especially in non-textured region…
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…
SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs
Guangyao Zhai, Xiaoni Cai, Dianye Huang +5
Object rearrangement is pivotal in robotic-environment interactions, representing a significant capability in embodied AI. In this paper, we present SG-Bot, a novel rearrangement f…
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction
Yan Di, Chenyangguang Zhang, Pengyuan Wang +6
In this paper, we present a novel shape reconstruction method leveraging diffusion model to generate 3D sparse point cloud for the object captured in a single RGB image. Recent met…
DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field
Chenyangguang Zhang, Yan Di, Ruida Zhang +4
Reconstructing hand-held objects from a single RGB image is an important and challenging problem. Existing works utilizing Signed Distance Fields (SDF) reveal limitations in compre…
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu +4
Controllable scene synthesis aims to create interactive environments for various industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applicat…
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction
Dekai Zhu, Guangyao Zhai, Yan Di +6
Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneous…
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +10
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks…
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung, Guangyao Zhai, Shun-Cheng Wu +10
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation…
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Yongzhi Su, Yan Di, Fabian Manhardt +5
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage me…
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
Guangyao Zhai, Dianye Huang, Shun-Cheng Wu +6
6-DoF robotic grasping is a long-lasting but unsolved problem. Recent methods utilize strong 3D networks to extract geometric grasping representations from depth sensors, demonstra…
DA Dataset: Toward Dexterity-Aware Dual-Arm Grasping
Guangyao Zhai, Yu Zheng, Ziwei Xu +6
In this paper, we introduce DA, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The da…
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +9
Depth estimation is a core task in 3D computer vision. Recent methods investigate the task of monocular depth trained with various depth sensor modalities. Every sensor has its adv…