5 citations · 8 across the 13 of their papers we have counts for
14 papers
ReFlow: Self-correction Motion Learning for Dynamic Scene Reconstruction
Yanzhe Liang, Ruijie Zhu, Hanzhi Chang +3
We present ReFlow, a unified framework for monocular dynamic scene reconstruction that learns 3D motion in a novel self-correction manner from raw video. Existing methods often suf…
GLASS: Geometry-aware Local Alignment and Structure Synchronization Network for 2D-3D Registration
Zhixin Cheng, Jiacheng Deng, Xinjun Li +5
Image-to-point cloud registration methods typically follow a coarse-to-fine pipeline, extracting patch-level correspondences and refining them into dense pixel-to-point matches. Ho…
GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation
Xujing Tao, Chuxin Wang, Yubo Ai +8
Open-vocabulary 3D semantic segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-voca…
Bridge 2D-3D: Uncertainty-aware Hierarchical Registration Network with Domain Alignment
Zhixin Cheng, Jiacheng Deng, Xinjun Li +2
The method for image-to-point cloud registration typically determines the rigid transformation using a coarse-to-fine pipeline. However, directly and uniformly matching image patch…
SAS: Segment Any 3D Scene with Integrated 2D Priors
Zhuoyuan Li, Jiahao Lu, Jiacheng Deng +4
The open vocabulary capability of 3D models is increasingly valued, as traditional methods with models trained with fixed categories fail to recognize unseen objects in complex dyn…
Beyond the Final Layer: Hierarchical Query Fusion Transformer with Agent-Interpolation Initialization for 3D Instance Segmentation
Jiahao Lu, Jiacheng Deng, Tianzhu Zhang
3D instance segmentation aims to predict a set of object instances in a scene and represent them as binary foreground masks with corresponding semantic labels. Currently, transform…
DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering
Jiahao Lu, Jiacheng Deng, Ruijie Zhu +4
Dynamic scenes rendering is an intriguing yet challenging problem. Although current methods based on NeRF have achieved satisfactory performance, they still can not reach real-time…
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
Ruijie Zhu, Yanzhe Liang, Hanzhi Chang +5
Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Altho…
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection
Jiacheng Deng, Jiahao Lu, Tianzhu Zhang
3D object detection is essential for understanding 3D scenes. Contemporary techniques often require extensive annotated training data, yet obtaining point-wise annotations for poin…
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
Zhuoyuan Li, Yubo Ai, Jiahao Lu +7
Transformers have demonstrated impressive results for 3D point cloud semantic segmentation. However, the quadratic complexity of transformer makes computation costs high, limiting…
Unsupervised Template-assisted Point Cloud Shape Correspondence Network
Jiacheng Deng, Jiahao Lu, Tianzhu Zhang
Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds. Existing methods obtain correspondences directly…
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
Jiahao Lu, Jiacheng Deng, Tianzhu Zhang
3D instance segmentation (3DIS) is a crucial task, but point-level annotations are tedious in fully supervised settings. Thus, using bounding boxes (bboxes) as annotations has show…
Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection
ChuXin Wang, Wenfei Yang, Tianzhu Zhang
Semi-supervised 3D object detection from point cloud aims to train a detector with a small number of labeled data and a large number of unlabeled data. The core of existing methods…
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Ziyang Song, Ruijie Zhu, Chuxin Wang +3
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically trained and tested…