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Chenyangguang Zhang

16 accepted papers

2026

FUN REC * Reconstructing Functional 3D Scenes from Egocentric Interaction Videos

Alexandros Delitzas, Chenyangguang Zhang, Alexey Gavryushin, Tommaso Di Mario, Boyang Sun, Rishabh Dabral +5
CVPR 2026

We present FunREC, a method for reconstructing functional 3D digital twins of indoor scenes directly from egocentric RGB-D interaction videos. Unlike existing methods on articulated reconstruction, which rely on controlled setups, multi-state captures, or CAD priors, FunREC operates directly on in-t

Cited by 0Source ↗code ↗Scholar ↗
2026

FlexAvatar: Flexible Large Reconstruction Model for Animatable Gaussian Head Avatars with Detailed Deformation

Cheng Peng, Zhuo Su, Liao Wang, Chen Guo, Zhaohu Li, Chengjiang Long +4
CVPR 2026

We present FlexAvatar, a flexible large reconstruction model for high-fidelity 3D head avatars with detailed dynamic deformation from single or sparse images, without requiring camera poses or expression labels. It leverages a transformer-based reconstruction model with structured head query tokens

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2025

DyGS-SLAM: Real-Time Accurate Localization and Gaussian Reconstruction for Dynamic Scenes

Xinggang Hu, Chenyangguang Zhang, Mingyuan Zhao, Yuanze Gui, Xiangkui Zhang, Xiangyang Ji
ICCV 2025poster

In dynamic scenes, achieving accurate camera localization and reconstructing a long-term consistent map containing only the static background are two major challenges faced by Visual Simultaneous Localization and Mapping (VSLAM). In current traditional dynamic VSLAM systems, the methods used to hand…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

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

Ziqin Huang, Gu Wang, Chenyangguang Zhang, Ruida Zhang, Xiu Li, Xiangyang Ji
CVPR 2025poster

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 response, RGB-based methods have been developed. Among these methods, geometry-guided pose regression that originated from…

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2025

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

Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang, Ruida Zhang, Xiangyang Ji, Marc Pollefeys +1
CVPR 2025highlight

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 relationships of objects, functional 3D scene graphs capture objects, interactive elements, and their functional relatio…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2025

Street Gaussians without 3D Object Tracker

Ruida Zhang, Chengxi Li, Chenyangguang Zhang, Xingyu Liu, Haili Yuan, Yanyan Li +2
ICCV 2025poster

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 poses to reconstruct dynamic objects in canonical space and move them based on these poses during rendering. While some…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

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

Xingyu Liu, Gu Wang, Ruida Zhang, Chenyangguang Zhang, Federico Tombari, Xiangyang Ji
CVPR 2025poster

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 estimate the unseen object's pose through a single unposed RGB-D reference image. While previous works leverage reference image…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction

Bowen Fu*, Gu Wang*, Chenyangguang Zhang, Yan Di, Ziqin Huang, Zhiying Leng +3
ECCV 2024poster

"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 employ a point cloud denoising diffusion model to account for the probabilistic nature of this problem. In the core, we int…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation

Ruida Zhang, Chenyangguang Zhang, Yan Di, Fabian Manhardt, Xingyu Liu, Federico Tombari +1
CVPR 2024poster

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 geometrically similar CAD models from a pre-processed database to tightly match the target. Unlike existing dense matching based m…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation

Ruida Zhang, Ziqin Huang, Gu Wang, Chenyangguang Zhang, Yan Di, Xingxing Zuo +2
ECCV 2024poster

"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 efforts have turned to RGB-based methods; however, they face significant challenges stemming from the absence of depth i…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2024

MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision

Chenyangguang Zhang, Guanlong Jiao, Yan Di, Gu Wang, Ziqin Huang, Ruida Zhang +4
CVPR 2024poster

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 contrast readily accessible hand-object videos offer a promising training data source but they only give heavily occluded object…

Cited by 11Source ↗PDF ↗Scholar ↗
2024

RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications

Xingyu Liu, Chenyangguang Zhang, Gu Wang, Ruida Zhang, Xiangyang Ji
ICRA 2024poster

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-real domain gap. While mainstream works tackle this problem in the RGB domain, we focus on depth data synthesis and dev…

Cited by 0Source ↗code ↗Scholar ↗
2024

SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation

Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang +3
CVPR 2024poster

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. Existing works utilizing mean shapes often fall short of capturing this variation. To address this issue we present SecondPose…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗
2024

ShapeMatcher: Self-Supervised Joint Shape Canonicalization Segmentation Retrieval and Deformation

Yan Di, Chenyangguang Zhang, Chaowei Wang, Ruida Zhang, Guangyao Zhai, Yanyan Li +3
CVPR 2024poster

In this paper we present ShapeMatcher a unified self-supervised learning framework for joint shape canonicalization segmentation retrieval and deformation. Given a partially-observed object in an arbitrary pose we first canonicalize the object by extracting point-wise affine invariant features disen…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2023

DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field

Chenyangguang Zhang, Yan Di, Ruida Zhang, Guangyao Zhai, Fabian Manhardt, Federico Tombari +1
NeurIPS 2023poster

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 comprehensively capturing the complex hand-object interactions, since SDF is only reliable within the proximity of the target…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗
2023

U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds

Yan Di, Chenyangguang Zhang, Ruida Zhang, Fabian Manhardt, Yongzhi Su, Jason Rambach +3
ICCV 2023poster

In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly…

Cited by 3PDF ↗code ↗Scholar ↗